subject:(Convolutional neural network)

California State University – Sacramento

1. Rawlani, Bhagyashree. Distracted driver detection using Capsule Network.

Degree: MS, Computer Science, 2019, California State University – Sacramento

URL: http://hdl.handle.net/10211.3/207659

► Convolutional neural networks are generally assumed to be the best neural networks for classifying images. In November 2017, Geoffrey Hinton et al. introduced another neural… (more)

Subjects/Keywords: Convolutional neural network

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APA (6^th Edition):

Rawlani, B. (2019). Distracted driver detection using Capsule Network. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207659

Chicago Manual of Style (16^th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Masters Thesis, California State University – Sacramento. Accessed January 20, 2021. http://hdl.handle.net/10211.3/207659.

MLA Handbook (7^th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Web. 20 Jan 2021.

Vancouver:

Rawlani B. Distracted driver detection using Capsule Network. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10211.3/207659.

Council of Science Editors:

Rawlani B. Distracted driver detection using Capsule Network. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207659

University of Illinois – Urbana-Champaign

2. Yeh, Raymond Alexander. Stable and symmetric convolutional neural network.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/92687

► First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max-pooling, and Relu non-linearity are stable to additive noise. Second, we explore… (more)

Subjects/Keywords: convolutional neural network; deep learning

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APA (6^th Edition):

Yeh, R. A. (2016). Stable and symmetric convolutional neural network. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/92687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Web. 20 Jan 2021.

Vancouver:

Yeh RA. Stable and symmetric convolutional neural network. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/92687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeh RA. Stable and symmetric convolutional neural network. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

3. Zhu, Tianyilin. Lipreading with convolutional and recurrent neural network models.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/97763

► Lip reading is the process of speech recognition from solely visual information. The goal of this thesis is to perform a silence vs. speech classification,… (more)

Subjects/Keywords: Lipreading; Convolutional neural network

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APA (6^th Edition):

Zhu, T. (2017). Lipreading with convolutional and recurrent neural network models. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97763

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhu, Tianyilin. “Lipreading with convolutional and recurrent neural network models.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/97763.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhu, Tianyilin. “Lipreading with convolutional and recurrent neural network models.” 2017. Web. 20 Jan 2021.

Vancouver:

Zhu T. Lipreading with convolutional and recurrent neural network models. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/97763.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhu T. Lipreading with convolutional and recurrent neural network models. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97763

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

4. Snuverink, Iris (author). Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2

► In hyperspectral (HS) imaging, for every pixel a spectrum of wavelengths is captured. These spectra represent material properties, i.e. the spectral signatures. So, classification of… (more)

▼ In hyperspectral (HS) imaging, for every pixel a spectrum of wavelengths is captured. These spectra represent material properties, i.e. the spectral signatures. So, classification of HS imagery is based on material properties. This thesis describes a framework to perform pixelwise classification of HS images of a fixed scene subject to varying ambient conditions. TNO has recorded HS images over the course of one year, every hour between sunrise and sunset. Therefore, this data set is subject to a range of lighting, weather and seasonal conditions, degrading the recorded data. Traditionally, atmospheric models are used to correct for these effects, recovering spectral information. In this work a Fully Convolutional Neural Network (FCN) is trained to perform the segmentation task which also learns to correct for ambient conditions, eliminating the need of implementing an atmospheric model or applying image normalization. A single FCN (U-Net) is implemented to solve a five-class segmentation problem, distinguishing broad leaf trees, grass, sand, asphalt and artificial grass. To start training a neural network, the training data set requires a corresponding ground truth. A sparsely annotated mask is designed which fits images covering the entire recording period. In single-scene segmentation, annotating using a sparse mask is a quick method which allows for moving object borders. Furthermore, it avoids inclusion of mixed pixels. In order to reduce computational load, the training data set is formed by many small patches taken from the original HS images. Consequently, the network is trained with local spectral-spatial information. Training the standard U-Net proves to be limited to training data sets under relatively constant ambient conditions. In order to further enhance generalization over seasons, the network weights are rearranged so that a similar number of weights is maintained. This network is essential for training a complex training data set, as it is able to extract more informative features. The standard U-Net trained with a simple training data set (i.e. relatively constant ambient conditions) achieves an accuracy A > 94% for both sunny and rainy test images irrespective of time of the day. However, this model is valid for a limited period of time. The customized U-Net trained with a complex training data set (i.e. highly varying ambient conditions) yields segmentations with an accuracy ranging between 86 and 93%. This model is valid for a longer period of time, covering multiple seasons. So the experiments show that there is a trade-off between segmentation accuracy and duration of model validity, which is controlled by network weight arrangement. Advisors/Committee Members: Bliek, Laurens (mentor), Verhaegen, Michel (mentor), Schutte, Klamer (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Deep Learning; Convolutional Neural Network; Fully Convolutional Neural network; Image segmentation

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APA (6^th Edition):

Snuverink, I. (. (2017). Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2

Chicago Manual of Style (16^th Edition):

Snuverink, Iris (author). “Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions.” 2017. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2.

MLA Handbook (7^th Edition):

Snuverink, Iris (author). “Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions.” 2017. Web. 20 Jan 2021.

Vancouver:

Snuverink I(. Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2.

Council of Science Editors:

Snuverink I(. Deep Learning for Pixelwise Classification of Hyperspectral Images: A generalizing model for a fixed scene subject to temporally changing weather, lighting and seasonal conditions. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:e6ce206b-09d1-4511-8349-b5e67f96c2d2

University of Ottawa

5. Zhang, Huizhen. Alpha Matting via Residual Convolutional Grid Network .

Degree: 2019, University of Ottawa

URL: http://hdl.handle.net/10393/39467

► Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and… (more)

Subjects/Keywords: Alpha matting; Convolutional Neural Network; Grid Network

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APA (6^th Edition):

Zhang, H. (2019). Alpha Matting via Residual Convolutional Grid Network . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/39467

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhang, Huizhen. “Alpha Matting via Residual Convolutional Grid Network .” 2019. Thesis, University of Ottawa. Accessed January 20, 2021. http://hdl.handle.net/10393/39467.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhang, Huizhen. “Alpha Matting via Residual Convolutional Grid Network .” 2019. Web. 20 Jan 2021.

Vancouver:

Zhang H. Alpha Matting via Residual Convolutional Grid Network . [Internet] [Thesis]. University of Ottawa; 2019. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/10393/39467.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang H. Alpha Matting via Residual Convolutional Grid Network . [Thesis]. University of Ottawa; 2019. Available from: http://hdl.handle.net/10393/39467

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

6. WU, JHE-WEI. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.

Degree: Master, Information Management, 2017, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

► The rapid increase of computing power, along with the improvement of software capabilities has made artificial intelligence a new trend. Deep learning recently becomes the… (more)

Subjects/Keywords: Convolutional Neural Network; Artificial Intelligence; Stock investment

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APA (6^th Edition):

WU, J. (2017). Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

WU, JHE-WEI. “Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.” 2017. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

WU, JHE-WEI. “Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy.” 2017. Web. 20 Jan 2021.

Vancouver:

WU J. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

WU J. Applying Convolution Neural Network in Deep Learning to Predict on Stock Trading Strategy. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-145700

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Texas

7. Sure, Venkata Leela. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.

Degree: 2019, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc1538703/

► Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. To… (more)

Subjects/Keywords: Convolutional Neural Network; Medical Image Classification

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APA (6^th Edition):

Sure, V. L. (2019). Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1538703/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sure, Venkata Leela. “Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.” 2019. Thesis, University of North Texas. Accessed January 20, 2021. https://digital.library.unt.edu/ark:/67531/metadc1538703/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sure, Venkata Leela. “Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.” 2019. Web. 20 Jan 2021.

Vancouver:

Sure VL. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. [Internet] [Thesis]. University of North Texas; 2019. [cited 2021 Jan 20]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538703/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sure VL. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. [Thesis]. University of North Texas; 2019. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538703/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

8. Nordeng, Ian Edward. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.

Degree: MS, Mechanical Engineering, 2018, University of North Dakota

URL: https://commons.und.edu/theses/2300

► This work presents a novel system utilizing previously developed convolutional neural network (CNN) architectures to aid in automating maintenance inspections of the dead-end body… (more)

Subjects/Keywords: CNN; Convolutional Neural Network; Inspections; Machine Learning

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APA (6^th Edition):

Nordeng, I. E. (2018). Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. (Masters Thesis). University of North Dakota. Retrieved from https://commons.und.edu/theses/2300

Chicago Manual of Style (16^th Edition):

Nordeng, Ian Edward. “Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.” 2018. Masters Thesis, University of North Dakota. Accessed January 20, 2021. https://commons.und.edu/theses/2300.

MLA Handbook (7^th Edition):

Nordeng, Ian Edward. “Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.” 2018. Web. 20 Jan 2021.

Vancouver:

Nordeng IE. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. [Internet] [Masters thesis]. University of North Dakota; 2018. [cited 2021 Jan 20]. Available from: https://commons.und.edu/theses/2300.

Council of Science Editors:

Nordeng IE. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. [Masters Thesis]. University of North Dakota; 2018. Available from: https://commons.und.edu/theses/2300

Delft University of Technology

9. Trommel, Kars (author). Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb

►

During the preparation for the Olympic Sailing Competition, held in 2021 in Tokyo, Japan, the Dutch National Sailing Team encountered days with unpredicted wind behaviour.… (more)

Subjects/Keywords: Unsupervised; Wind; Classification; Convolutional; Neural; Network; Autoencoder

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APA (6^th Edition):

Trommel, K. (. (2020). Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb

Chicago Manual of Style (16^th Edition):

Trommel, Kars (author). “Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan.” 2020. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb.

MLA Handbook (7^th Edition):

Trommel, Kars (author). “Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan.” 2020. Web. 20 Jan 2021.

Vancouver:

Trommel K(. Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb.

Council of Science Editors:

Trommel K(. Wind Classification using Unsupervised Learning: In support of the Olympic Sailing Competition in Tokyo, Japan. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:9ea87a34-0a19-4ed1-8c38-d43c94715ebb

University of Illinois – Urbana-Champaign

10. Liu, Xianming. Feedback convolutional neural network in applications of computer vision.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/95471

► With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network… (more)

▼ With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network goes deeper and wider, the demand for high quality supervised training data also increases exponentially with the model complexity. Considering the difficulty in data acquisition of high quality and complete labels, the topic of weakly-supervised learning raises much attention recently in both machine learning and computer vision communities. Inspired by the evidence from cognitive research, visual attention plays a critical role in human vision system. While bottom-up selectivity bias has been well studied from both mathematical and computer vision perspectives, the top-down selectivity that wraps semantic information has been seldom analyzed in this field. In this thesis, a more computationally efficient model for building the bottom-up selectivity of images has been built, using the scale space theory. The method uses a statistical way to build hierarchical presentations of image content on different scales, and an unsupervised approach to derive a measurement of “objectness,” which is further used as region proposal for object detection, and semantic segmentation. Motivated by the “biased competition theory,” which states that 1) a visual task is highly driven by goal or task, and 2) the unrelated neuron will be suppressed during a feedback loop in the human visual cortex, a computational model of feedback mechanism has been proposed to implement the top-down selectivity, and named as "Feedback Neural Network." The proposed feedback network optimizes the high-level task's target function by performing a feedback optimization to close irrelevant neurons in convolutional neural network. Experiments show it is effective at finding the salient regions with higher accuracy and efficiency given the high-level semantic labels. This technique is further used in weakly-supervised learning in computer vision, where only partial supervision is given during training – for example, object localization, and semantic segmentation, using only categorical image level labels. We report experiments on the ImageNet object localization dataset as well as satellite image analysis dataset to demonstrate the effectiveness of the algorithm. The feedback network could also be used to improve the image classification performance, by "looking and thinking twice." Cognitive study suggests when human subjects are given more time to observe the visual signal, there is a time at which the recognition accuracy dramatically increases. By cropping the salient regions where the network learns from large-scale imagery data, the network will reduce the chance of miss-classification by eliminating background and context. We show that this strategy improves ImageNet classification by more than 1% in the top 1 result. Especially for objects of small size, the improvement is even larger. A binarized feedback optimization algorithm is also proposed in this thesis,… Advisors/Committee Members: Huang, Thomas S (advisor), Huang, Thomas S (Committee Chair), Liang, Zhi-Pei (committee member), Hasegawa-Johnson, Mark (committee member), Fu, Wai-Tat (committee member).

Subjects/Keywords: Convolutional Neural Network; Feedback; Computer Vision

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APA (6^th Edition):

Liu, X. (2016). Feedback convolutional neural network in applications of computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95471

Chicago Manual of Style (16^th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/95471.

MLA Handbook (7^th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Web. 20 Jan 2021.

Vancouver:

Liu X. Feedback convolutional neural network in applications of computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/95471.

Council of Science Editors:

Liu X. Feedback convolutional neural network in applications of computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95471

University of Illinois – Urbana-Champaign

11. Qian, Kaizhi. Speech enhancement using deep dilated CNN.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/101644

► In recent years, deep learning has achieved great success in speech enhancement. However, there are two major limitations regarding existing works. First, the Bayesian framework… (more)

▼ In recent years, deep learning has achieved great success in speech enhancement. However, there are two major limitations regarding existing works. First, the Bayesian framework is not adopted in many such deep-learning-based algorithms. In particular, the prior distribution for speech in the Bayesian framework has been shown useful by regularizing the output to be in the speech space, and thus improving the performance. Second, the majority of the existing methods operate on the frequency domain of the noisy speech, such as spectrogram and its variations. We propose a Bayesian speech enhancement framework, called BaWN (Bayesian WaveNet), which directly operates on raw audio samples. It adopts the recently announced WaveNet, which is shown to be effective in modeling conditional distributions of speech samples while generating natural speech. Experiments show that BaWN is able to recover clean and natural speech. Multi-channel speech enhancement with ad-hoc sensors has been a challenging task. Speech model guided beamforming algorithms are able to recover natural sounding speech, but the speech models tend to be oversimplified to prevent the inference from becoming too complicated. On the other hand, deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference, but they are unable to deal with variable number of input channels. Also, deep learning approaches introduce a lot of errors, particularly in the presence of unseen noise types and settings. We have therefore proposed an enhancement framework called DeepBeam, which combines the two complementary classes of algorithms. DeepBeam introduces a beamforming filter to produce natural sounding speech, but the filter coefficients are determined with the help of a monaural speech enhancement neural network. Experiments on synthetic and real-world data show that DeepBeam is able to produce clean, dry and natural sounding speech, and is robust against unseen noise. Advisors/Committee Members: Hasegawa-Johnson, Mark (advisor).

Subjects/Keywords: speech enhancement; convolutional neural network; beamforming

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APA (6^th Edition):

Qian, K. (2018). Speech enhancement using deep dilated CNN. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Qian, Kaizhi. “Speech enhancement using deep dilated CNN.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/101644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Qian, Kaizhi. “Speech enhancement using deep dilated CNN.” 2018. Web. 20 Jan 2021.

Vancouver:

Qian K. Speech enhancement using deep dilated CNN. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/101644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Qian K. Speech enhancement using deep dilated CNN. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

12. Yu, Xianhua. Sequential neural network decoder for convolutional code with large block sizes.

Degree: 2020, Iowa State University

URL: https://lib.dr.iastate.edu/etd/18252

► Due to the curse of dimensionality, the training complexity of the neural network based channel-code decoder increases exponentially along with the code word’s length. Although… (more)

Subjects/Keywords: convolutional code; deep learning; neural network decoder

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APA (6^th Edition):

Yu, X. (2020). Sequential neural network decoder for convolutional code with large block sizes. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/18252

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yu, Xianhua. “Sequential neural network decoder for convolutional code with large block sizes.” 2020. Thesis, Iowa State University. Accessed January 20, 2021. https://lib.dr.iastate.edu/etd/18252.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yu, Xianhua. “Sequential neural network decoder for convolutional code with large block sizes.” 2020. Web. 20 Jan 2021.

Vancouver:

Yu X. Sequential neural network decoder for convolutional code with large block sizes. [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Jan 20]. Available from: https://lib.dr.iastate.edu/etd/18252.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu X. Sequential neural network decoder for convolutional code with large block sizes. [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/18252

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

13. Liu, Jia-Hong. Portfolio Investment Based on Neural Networks.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

► In this thesis, we combine the trading signals generated by the gen expression programming (GEP) method of Lee et al. and the portfolio generated by… (more)

Subjects/Keywords: neural network; stock investment; gene expression programming; convolutional neural network; portfolio

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, J. (2018). Portfolio Investment Based on Neural Networks. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liu, Jia-Hong. “Portfolio Investment Based on Neural Networks.” 2018. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liu, Jia-Hong. “Portfolio Investment Based on Neural Networks.” 2018. Web. 20 Jan 2021.

Vancouver:

Liu J. Portfolio Investment Based on Neural Networks. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu J. Portfolio Investment Based on Neural Networks. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621118-142449

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Mid Sweden University

14. Wang, Xutao. Chinese Text Classification Based On Deep Learning.

Degree: Information Systems and Technology, 2018, Mid Sweden University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

► Text classification has always been a concern in area of natural language processing, especially nowadays the data are getting massive due to the development… (more)

Subjects/Keywords: Text classification; Recurrent neural network; Convolutional neural network; Computer Systems; Datorsystem

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, X. (2018). Chinese Text Classification Based On Deep Learning. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Thesis, Mid Sweden University. Accessed January 20, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Xutao. “Chinese Text Classification Based On Deep Learning.” 2018. Web. 20 Jan 2021.

Vancouver:

Wang X. Chinese Text Classification Based On Deep Learning. [Internet] [Thesis]. Mid Sweden University; 2018. [cited 2021 Jan 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang X. Chinese Text Classification Based On Deep Learning. [Thesis]. Mid Sweden University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame

15. Tianchen Wang. Statistical Neural Networks: Concepts, Frameworks and Applications</h1>.

Degree: Computer Science and Engineering, 2020, University of Notre Dame

URL: https://curate.nd.edu/show/47429883t43

► As one of the fundamental issues in the fields such as computer vision and deep learning, accelerating the inference speed of the convolutional neural… (more)

▼ As one of the fundamental issues in the fields such as computer vision and deep learning, accelerating the inference speed of the convolutional neural network (CNN) has attracted a lot of attention, which is to reduce the gap between the desired high performance of deep CNN and the required huge amount of computation. Various techniques have been explored to address this challenge, such as network compression and quantization, lightweight networks targeting resource-constrained platforms, dynamic computation graphs providing efficient early exits, and, on top of all these techniques, network parallelization. Although a large body of works exists in neural network parallelization, most of them focus on the training phase only or distributing a batch of instances to multiple computation cores. None of these parallelization works, however, helps to reduce the inference latency of a single instance (such as an image), which is critical for real-time applications. Existing techniques for single image inference include operator parallelism and model parallelism. The former technique explores concurrency in operators such as convolution, and the latter distributes kernels of convolutional layers across multiple cores. These approaches do not offer good scalability and usually cannot fully utilize a large number of cores available in modern high-performance computing platforms. In this dissertation, we introduce a new general framework named statistical convolutional neural network (SCNN) to speedup the instance inference with the help of independent component analysis (ICA), which can be applied to various tasks. As a general framework, SCNN can be implemented with different neural network backbones to speedup, while it is orthogonal to the existing speedup methods. We use ICA to decompose the spatio-temporal correlated data and propagate the extracted essential features as the learning process. The performance evaluations of SCNN in video object detection and 3D cardiac cine MRI segmentation shows that SCNN could achieve a large speedup compared with the existing methods. We further introduce ICA-Net as an improvement of SCNN in accuracy, throughput, and latency. The improved framework is implemented and verified by multiple tasks including image classification, image object detection, video object detection, and 3D cardiac cine MRI segmentation. The latencies of the models are all largely reduced and the accuracies achieve state-of-the-art. Advisors/Committee Members: Jinjun Xiong, Committee Member, Meng Jiang, Committee Member, Walter Scheirer, Committee Member, Yiyu Shi, Research Director.

Subjects/Keywords: convolutional neural network; independent component analysis; neural network acceleration

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, T. (2020). Statistical Neural Networks: Concepts, Frameworks and Applications</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/47429883t43

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Tianchen. “Statistical Neural Networks: Concepts, Frameworks and Applications</h1>.” 2020. Thesis, University of Notre Dame. Accessed January 20, 2021. https://curate.nd.edu/show/47429883t43.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Tianchen. “Statistical Neural Networks: Concepts, Frameworks and Applications</h1>.” 2020. Web. 20 Jan 2021.

Vancouver:

Wang T. Statistical Neural Networks: Concepts, Frameworks and Applications</h1>. [Internet] [Thesis]. University of Notre Dame; 2020. [cited 2021 Jan 20]. Available from: https://curate.nd.edu/show/47429883t43.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang T. Statistical Neural Networks: Concepts, Frameworks and Applications</h1>. [Thesis]. University of Notre Dame; 2020. Available from: https://curate.nd.edu/show/47429883t43

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

16. Maji, Partha. Model-architecture co-design of deep neural networks for embedded systems.

Degree: PhD, 2020, University of Cambridge

URL: https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101

► In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions.… (more)

▼ In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions. An application running on an embedded system typically has limited access to memory resources, processing power, and storage. Implementing deep convolutional neural network-based inference on resource-constrained devices can be very challenging, as these environments cannot usually make use of the massive computing power and storage that are present in cloud server environments. Furthermore, the constantly evolving nature of modern deep network architecture aggravates the problem by making it necessary to balance flexibility against specialisation to avoid the inability to adapt. However, much of the baseline architecture of a deep convolutional neural network stayed the same. With careful optimisation of the most common and widely occurring layer architectures, it is typically possible to accelerate these emerging workloads for resource-constrained embedded systems. This thesis makes four contributions. I first developed a lossy three-stage low-rank approximation scheme that can reduce the computational complexity of a pre-trained model by 3-5x and up to 8-9x for individual convolutional layers. This scheme requires restructuring of the convolutional layers and generally suits the scenario where both the training data and trained model are available. In many scenarios, the training data is not available for fine-tuning any loss in prediction accuracy if structural changes are made to a model as a post-processing step. Besides the lack of availability of training data, there are other situations where the architecture of a model cannot be changed after training. My second contribution handles this scenario by using a low-level optimisation scheme that requires no changes to the model architecture, unlike the low-rank approximation scheme. This novel scheme uses a modified version of the Cook-Toom algorithm to reduce the computational intensity of commonly occurring dense and spatial convolutional layers and speedup inference time by 2-4x. My third contribution is an efficient implementation of the Cook-Toom class of algorithms on ubiquitous Arm's low-power Cortex processor. Unlike the direct convolution, computing convolutions using the modified Cook-Toom algorithm requires a different data processing pipeline as it involves pre- and post-transformations of the intermediate activations. I introduced a multi-channel multi-region (MCMR) scheme to enable an efficient implementation of the fast Cook-Toom algorithm. I demonstrate that by effectively using SIMD instructions and the MCMR scheme an average 2-3x and a peak 4x per layer speedup is easily achievable. My final contribution is the Cook-Toom accelerator, a custom hardware architecture for modern convolutional neural networks. This accelerator architecture is designed from the ground up to address some of the limitations of a resource-constrained SIMD processor. I also illustrate how new emerging…

Subjects/Keywords: Neural Network; Convolutional Neural Network; Optimisation; SIMD; Low-rank; Compression; Accelerators

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Maji, P. (2020). Model-architecture co-design of deep neural networks for embedded systems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101

Chicago Manual of Style (16^th Edition):

Maji, Partha. “Model-architecture co-design of deep neural networks for embedded systems.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 20, 2021. https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101.

MLA Handbook (7^th Edition):

Maji, Partha. “Model-architecture co-design of deep neural networks for embedded systems.” 2020. Web. 20 Jan 2021.

Vancouver:

Maji P. Model-architecture co-design of deep neural networks for embedded systems. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 20]. Available from: https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101.

Council of Science Editors:

Maji P. Model-architecture co-design of deep neural networks for embedded systems. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.54581 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.810101

University of Cambridge

17. Maji, Partha. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.

Degree: PhD, 2020, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/307488

► In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions.… (more)

▼ In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions. An application running on an embedded system typically has limited access to memory resources, processing power, and storage. Implementing deep convolutional neural network-based inference on resource-constrained devices can be very challenging, as these environments cannot usually make use of the massive computing power and storage that are present in cloud server environments. Furthermore, the constantly evolving nature of modern deep network architecture aggravates the problem by making it necessary to balance flexibility against specialisation to avoid the inability to adapt. However, much of the baseline architecture of a deep convolutional neural network stayed the same. With careful optimisation of the most common and widely occurring layer architectures, it is typically possible to accelerate these emerging workloads for resource-constrained embedded systems. This thesis makes four contributions. I first developed a lossy three-stage low-rank approximation scheme that can reduce the computational complexity of a pre-trained model by 3-5x and up to 8-9x for individual convolutional layers. This scheme requires restructuring of the convolutional layers and generally suits the scenario where both the training data and trained model are available. In many scenarios, the training data is not available for fine-tuning any loss in prediction accuracy if structural changes are made to a model as a post-processing step. Besides the lack of availability of training data, there are other situations where the architecture of a model cannot be changed after training. My second contribution handles this scenario by using a low-level optimisation scheme that requires no changes to the model architecture, unlike the low-rank approximation scheme. This novel scheme uses a modified version of the Cook-Toom algorithm to reduce the computational intensity of commonly occurring dense and spatial convolutional layers and speedup inference time by 2-4x. My third contribution is an efficient implementation of the Cook-Toom class of algorithms on ubiquitous Arm's low-power Cortex processor. Unlike the direct convolution, computing convolutions using the modified Cook-Toom algorithm requires a different data processing pipeline as it involves pre- and post-transformations of the intermediate activations. I introduced a multi-channel multi-region (MCMR) scheme to enable an efficient implementation of the fast Cook-Toom algorithm. I demonstrate that by effectively using SIMD instructions and the MCMR scheme an average 2-3x and a peak 4x per layer speedup is easily achievable. My final contribution is the Cook-Toom accelerator, a custom hardware architecture for modern convolutional neural networks. This accelerator architecture is designed from the ground up to address some of the limitations of a resource-constrained SIMD processor. I also illustrate how new emerging…

Subjects/Keywords: Neural Network; Convolutional Neural Network; Optimisation; SIMD; Low-rank; Compression; Accelerators

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Maji, P. (2020). Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/307488

Chicago Manual of Style (16^th Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 20, 2021. https://www.repository.cam.ac.uk/handle/1810/307488.

MLA Handbook (7^th Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Web. 20 Jan 2021.

Vancouver:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 20]. Available from: https://www.repository.cam.ac.uk/handle/1810/307488.

Council of Science Editors:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/307488

Tampere University

18. Zhou, Yi. Sentiment classification with deep neural networks .

Degree: 2019, Tampere University

URL: https://trepo.tuni.fi//handle/10024/116148

► Sentiment classification is an important task in Natural Language Processing (NLP) area. Deep neural networks become the mainstream method to perform the text sentiment classification… (more)

▼ Sentiment classification is an important task in Natural Language Processing (NLP) area. Deep neural networks become the mainstream method to perform the text sentiment classification nowadays. In this thesis two datasets are used. The first dataset is a hotel review dataset(TripAdvisor dataset) that collects the hotel reviews from the TripAdvisor website using Python Scrapy framework. The preprocessing steps are then applied to clean the dataset. A record in the TripAdvisor dataset consists of the text review and corresponding sentiment score. There are 5 sentimental labels: very negative, negative, neutral, positive, and very positive. The second dataset is the Stanford Sentiment Treebank (SST) dataset. It is a public and common dataset for sentiment classification. Text Convolutional Neural Network (Text-CNN), Very Deep Convolutional Neural Network (VDCNN), and Bidirectional Long Short Term Memory neural network (BiLSTM) were chosen as different methods for the evaluation in the experiments. The Text-CNN was the first work to apply convolutional neural network architecture for the text classification. The VD-CNN applied deep convolutional layers, with up to 29 layers, to perform the text classification. The BiLSTM exploited the bidirectional recurrent neural network with long short term memory cell mechanism. On the other hand, word embedding techniques are also considered as an important factor in sentiment classification. Thus, in this thesis, GloVe and FastText techniques were used to investigate the effect of word embedding initialization on the dataset. GloVe is a unsupervised word embedding learning algorithm. FastText uses shallow neural network to generate word vectors and it has fast convergence speed for training and high speed for inference. The experiment was implemented using PyTorch framework. It shows that the BiLSTM with GloVe as the word vector initialization achieved the highest accuracy 73.73% while the VD-CNN with FastText had the lowest accuracy 71.95% on the TripAdvisor dataset. The BiLSTM model achieved 0.68 F1-score while the VD-CNN model obtained 0.67 F1-score on the TripAdvisor dataset. On the SST dataset, BiLSTM with GloVe again achieved the highest accuracy 36.35% and 0.35 F1-score. The VD-CNN model with GloVe had the worst evaluation result in terms of accuracy and F1-score. The Text-CNN model performed better than the VD-CNN model even thought the VD-CNN model has more layers in most cases. By analyzing the misclassified reviews in the TripAdvisor dataset from the three deep neural networks, it is shown that the hotel reviews with more contradictory sentimental words were more prone to misclassification than other hotel reviews.

Subjects/Keywords: deep neural networks; convolutional neural network; recurrent neural network; sentiment classification; hotel reviews; TripAdvisor

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhou, Y. (2019). Sentiment classification with deep neural networks . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi//handle/10024/116148

Chicago Manual of Style (16^th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Masters Thesis, Tampere University. Accessed January 20, 2021. https://trepo.tuni.fi//handle/10024/116148.

MLA Handbook (7^th Edition):

Zhou, Yi. “Sentiment classification with deep neural networks .” 2019. Web. 20 Jan 2021.

Vancouver:

Zhou Y. Sentiment classification with deep neural networks . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2021 Jan 20]. Available from: https://trepo.tuni.fi//handle/10024/116148.

Council of Science Editors:

Zhou Y. Sentiment classification with deep neural networks . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi//handle/10024/116148

Delft University of Technology

19. Hoogendoorn, Jasper (author). Sequential Monte Carlo method for training Neural Networks on non-stationary time series.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c

►

In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can… (more)

Subjects/Keywords: sequential Monte Carlo; Neural Networks; Time Series Forecasting; Convolutional Neural Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hoogendoorn, J. (. (2019). Sequential Monte Carlo method for training Neural Networks on non-stationary time series. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c

Chicago Manual of Style (16^th Edition):

Hoogendoorn, Jasper (author). “Sequential Monte Carlo method for training Neural Networks on non-stationary time series.” 2019. Masters Thesis, Delft University of Technology. Accessed January 20, 2021. http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c.

MLA Handbook (7^th Edition):

Hoogendoorn, Jasper (author). “Sequential Monte Carlo method for training Neural Networks on non-stationary time series.” 2019. Web. 20 Jan 2021.

Vancouver:

Hoogendoorn J(. Sequential Monte Carlo method for training Neural Networks on non-stationary time series. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 20]. Available from: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c.

Council of Science Editors:

Hoogendoorn J(. Sequential Monte Carlo method for training Neural Networks on non-stationary time series. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:659e9fd5-d251-46fe-8455-3a17bdd4f48c

University of Cincinnati

20. Sarpangala, Kishan. Semantic Segmentation Using Deep Learning Neural Architectures.

Degree: MS, Engineering and Applied Science: Computer Science, 2019, University of Cincinnati

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304

► In many picture handling methods, the capacity to segment items of concern automatically is a helpful pre-processing phase. There are several algorithms segmenting pictures depending… (more)

▼ In many picture handling methods, the capacity to segment items of concern automatically is a helpful pre-processing phase. There are several algorithms segmenting pictures depending on one or more parameters or previous understanding of the necessary category of items. While these algorithms generate visually attractive segmentations, their complexities are making many of them computationally costly.One of the approaches which has received a lot of traction recently is semantic segmentation. It’s a process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car). Semantic segmentation of a picture has recently been addressed by deep end-to-end neural networks.One of the main problems among all architectures is to consider the global visual context of the input to improve this segmentation forecasting. State of the art drawings use architectures that try to link different components of the image in order to understand the interactions between the items. To solve this problem, the concept of KeyPoint recognition is introduced within semantic segmentation by researchers. A KeyPoint recognition can be described as a procedure to find the most repeatable object or key points for the specific target object during the training phase.Thus, a general semantic segmentation algorithm gets enhanced to a global level which ensures understanding of a scene at the top level. This approach moves much of the computational burden to the training phase, without sacrificing recognition efficiency. Therefore, the resulting algorithm is robust, precise and fast enough for frame rate efficiency.General global semantic segmentation algorithms generate super pixels based solely on color. However, limits of objects do not necessarily overlap with contours of color, allowing segmentation in different situations to fail. In such instances, information beyond color channels is needed to achieve significant and accurate segmentations. A trend in software for picture and video acquisition is not only recording color but also a range of extra data, such as infrared values and depth.This research discusses a completely new method in which pictures are segmented into a required number of super pixels. These super pixels are generated using global semantic segmentation over a whole image, to ensure an extremely precise comprehension of the environment a new concept of integrating various channels beyond color channels to use the extra data accessible to enhance the outcomes of segmentation is used. The accomplished pixel-wise prediction is helpful for certain scenarios like action recognition, video captioning, or visual issues answering, scene understanding is also addressed.Together with multi-task loss, this segmentation approach task should help improve the understanding of a scene globally.This new segmentation approach accomplishes an improvement of more than 4 percent in border precision over regular segmentation which uses only color-based segmentation.We discuss several deep learning models… Advisors/Committee Members: Purdy, Carla (Committee Chair).

Subjects/Keywords: Artificial Intelligence; Semantic Segmentation; Convolutional Neural Network; Computer Vision; Deep Learning Neural Network; Artificial Intelligence; Fully Convolutional Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sarpangala, K. (2019). Semantic Segmentation Using Deep Learning Neural Architectures. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304

Chicago Manual of Style (16^th Edition):

Sarpangala, Kishan. “Semantic Segmentation Using Deep Learning Neural Architectures.” 2019. Masters Thesis, University of Cincinnati. Accessed January 20, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

MLA Handbook (7^th Edition):

Sarpangala, Kishan. “Semantic Segmentation Using Deep Learning Neural Architectures.” 2019. Web. 20 Jan 2021.

Vancouver:

Sarpangala K. Semantic Segmentation Using Deep Learning Neural Architectures. [Internet] [Masters thesis]. University of Cincinnati; 2019. [cited 2021 Jan 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

Council of Science Editors:

Sarpangala K. Semantic Segmentation Using Deep Learning Neural Architectures. [Masters Thesis]. University of Cincinnati; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304

University of Windsor

21. Ragunathan, Kumaran. Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks.

Degree: MS, Computer Science, 2020, University of Windsor

URL: https://scholar.uwindsor.ca/etd/8308

► Link Prediction (LP) in social networks (SN) is referred to as predicting the likelihood of a link formation in SNs in the near future. There… (more)

▼ Link Prediction (LP) in social networks (SN) is referred to as predicting the likelihood of a link formation in SNs in the near future. There are several types of SNs that are available such as human interaction network, biological network, protein-to-protein interaction network, and so on. Earlier LP researches used heuristics methods, including Common Neighbors, Resource Allocation, and many other similarity score methods. Even though heuristics methods perform better in some types of SNs, their performance is limited in other types of SNs. Finding the best heuristics for a given type of SN is a trial and error process. Recent state-of-the-art research, WLNM and SEAL showed that with deep learning techniques and subgraphing, the heuristics selection could be automated and increase the accuracy of LP. However, WLNM and SEAL have some limitations and still having performance lack in some types of SNs. The objective of this paper is to introduce a novel framework that overcomes the limitations of state-of-the-art methods and improves the accuracy of LP over various types of social networks. We propose a Link Prediction framework called PLACN that analyzes common neighbors based subgraphs using deep learning technique to predict links. PLACN is equipped with two new algorithms that are a subgraph extraction algorithm that efficiently extracts common neighbors of targeted nodes and a proposed new node labeling algorithm based on hop number and average path weight that creates consistent node orders over subgraphs. In addition to the algorithms, we derived a formula based on network properties to find an optimal number node for a given SN. PLACN converts the LP problem into an Image Classification problem and utilizes a Convolutional Neural Network to classify the links. We tested the proposed PLACN on seven different types of real-work networks and compared the performance against heuristics, latent methods, and state-of-the-art methods. Our results show that PLACN outperformed the compared Link Prediction methods while reaching above 96% AUC in tested benchmark social networks. Advisors/Committee Members: Ziad Kobti.

Subjects/Keywords: Convolutional Neural Network; Link Prediction; Machine Learning; PLACN; Social Network Analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ragunathan, K. (2020). Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/8308

Chicago Manual of Style (16^th Edition):

Ragunathan, Kumaran. “Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks.” 2020. Masters Thesis, University of Windsor. Accessed January 20, 2021. https://scholar.uwindsor.ca/etd/8308.

MLA Handbook (7^th Edition):

Ragunathan, Kumaran. “Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks.” 2020. Web. 20 Jan 2021.

Vancouver:

Ragunathan K. Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks. [Internet] [Masters thesis]. University of Windsor; 2020. [cited 2021 Jan 20]. Available from: https://scholar.uwindsor.ca/etd/8308.

Council of Science Editors:

Ragunathan K. Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks. [Masters Thesis]. University of Windsor; 2020. Available from: https://scholar.uwindsor.ca/etd/8308

University of Illinois – Urbana-Champaign

22. Yan, Zhicheng. Image recognition, semantic segmentation and photo adjustment using deep neural networks.

Degree: PhD, Computer Science, 2016, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/90724

► Deep Neural Networks (DNNs) have proven to be effective models for solving various problems in computer vision. Multi-Layer Perceptron Networks, Convolutional Neural Networks and Recurrent… (more)

▼ Deep Neural Networks (DNNs) have proven to be effective models for solving various problems in computer vision. Multi-Layer Perceptron Networks, Convolutional Neural Networks and Recurrent Neural Networks are representative examples of DNNs in the setting of supervised learning. The key ingredients in the successful development of DNN-based models include but not limited to task-specific designs of network architecture, discriminative feature representation learning and scalable training algorithms. In this thesis, we describe a collection of DNN-based models to address three challenging computer vision tasks, namely large-scale visual recognition, image semantic segmentation and automatic photo adjustment. For each task, the network architecture is carefully designed on the basis of the nature of the task. For large-scale visual recognition, we design a hierarchical Convolutional Neural Network to fully exploit a semantic hierarchy among visual categories. The resulting model can be deemed as an ensemble of specialized classifiers. We improve state-of-the-art results at an affordable increase of the computational cost. For image semantic segmentation, we integrate convolutional layers with novel spatially recurrent layers for incorporating global contexts into the prediction process. The resulting hybrid network is capable of learning improved feature representations, which lead to more accurate region recognition and boundary localization. Combined with a post-processing step involving a fully-connected conditional random field, our hybrid network achieves new state-of-the-art results on a large benchmark dataset. For automatic photo adjustment, we take a data-driven approach to learn the underlying color transforms from manually enhanced examples. We formulate the learning problem as a regression task, which can be approached with a Multi-Layer Perceptron network. We concatenate global contextual features, local contextual features as well as pixel-wise features and feed them into the deep network. State-of-the-art results are achieved on datasets with both global and local stylized adjustments. Advisors/Committee Members: Yu, Yizhou (advisor), Lazebnik, Svetlana (Committee Chair), Forsyth, David (committee member), Cao, Liangliang (committee member).

Subjects/Keywords: Deep Neural Network; Image Recognition; Semantic Segmentation; Photo Adjustment; Convolutional Neural Network; Recurrent Neural Network; Multi-Layer Perceptron Network

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APA (6^th Edition):

Yan, Z. (2016). Image recognition, semantic segmentation and photo adjustment using deep neural networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90724

Chicago Manual of Style (16^th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 20, 2021. http://hdl.handle.net/2142/90724.

MLA Handbook (7^th Edition):

Yan, Zhicheng. “Image recognition, semantic segmentation and photo adjustment using deep neural networks.” 2016. Web. 20 Jan 2021.

Vancouver:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/2142/90724.

Council of Science Editors:

Yan Z. Image recognition, semantic segmentation and photo adjustment using deep neural networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90724

Brno University of Technology

23. Ustsinau, Usevalad. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/189317

► The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the… (more)

Subjects/Keywords: Medical Imaging; Brain Tumour; Convolutional Neural Network; Segmentation; U-Net; Medical Imaging; Brain Tumour; Convolutional Neural Network; Segmentation; U-Net

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APA (6^th Edition):

Ustsinau, U. (2020). Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189317

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ustsinau, Usevalad. “Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning.” 2020. Thesis, Brno University of Technology. Accessed January 20, 2021. http://hdl.handle.net/11012/189317.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ustsinau, Usevalad. “Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning.” 2020. Web. 20 Jan 2021.

Vancouver:

Ustsinau U. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 20]. Available from: http://hdl.handle.net/11012/189317.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ustsinau U. Segmentace nádorů mozku v MRI datech s využitím hloubkového učení: Segmentation of brain tumours in MRI images using deep learning. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189317

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Bridgeport

24. Ali Albelwi, Saleh. Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition .

Degree: 2018, University of Bridgeport

URL: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040

► Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding… (more)

▼ Recent advances in Convolutional Neural Networks (CNNs) have obtained promising results in difficult deep learning tasks. However, the success of a CNN depends on finding an architecture to fit a given problem. A hand-crafted architecture is a challenging, time-consuming process that requires expert knowledge and effort, due to a large number of architectural design choices. In this dissertation, we present an efficient framework that automatically designs a high-performing CNN architecture for a given problem. In this framework, we introduce a new optimization objective function that combines the error rate and the information learnt by a set of feature maps using deconvolutional networks (deconvnet). The new objective function allows the hyperparameters of the CNN architecture to be optimized in a way that enhances the performance by guiding the CNN through better visualization of learnt features via deconvnet. The actual optimization of the objective function is carried out via the Nelder-Mead Method (NMM). Further, our new objective function results in much faster convergence towards a better architecture. The proposed framework has the ability to explore a CNN architecture’s numerous design choices in an efficient way and also allows effective, distributed execution and synchronization via web services. Empirically, we demonstrate that the CNN architecture designed with our approach outperforms several existing approaches in terms of its error rate. Our results are also competitive with state-of-the-art results on the MNIST dataset and perform reasonably against the state-of-the-art results on CIFAR-10 and CIFAR-100 datasets. Our approach has a significant role in increasing the depth, reducing the size of strides, and constraining some convolutional layers not followed by pooling layers in order to find a CNN architecture that produces a high recognition performance. Moreover, we evaluate the effectiveness of reducing the size of the training set on CNNs using a variety of instance selection methods to speed up the training time. We then study how these methods impact classification accuracy. Many instance selection methods require a long run-time to obtain a subset of the representative dataset, especially if the training set is large and has a high dimensionality. One example of these algorithms is Random Mutation Hill Climbing (RMHC). We improve RMHC so that it performs faster than the original algorithm with the same accuracy.

Subjects/Keywords: Convolutional neural network; Convolutional neural networks architecture design; Correlation coefficient; Deconvolutional network; Deep learning; Objective function

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ali Albelwi, S. (2018). Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ali Albelwi, Saleh. “Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition .” 2018. Thesis, University of Bridgeport. Accessed January 20, 2021. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ali Albelwi, Saleh. “Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition .” 2018. Web. 20 Jan 2021.

Vancouver:

Ali Albelwi S. Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2021 Jan 20]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ali Albelwi S. Hyperparameter Optimization Of Deep Convolutional Neural Networks Architectures For Object Recognition . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rochester Institute of Technology

25. Yin, Peng Nien. Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network.

Degree: MS, Thomas H. Gosnell School of Life Sciences (COS), 2019, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10107

► Recently, deep learning techniques from the computer science field have dramatically improved the ability of computers to recognize objects in images. This raised the… (more)

▼ Recently, deep learning techniques from the computer science field have dramatically improved the ability of computers to recognize objects in images. This raised the possibility of fully automated computer-aided diagnosis in the medical field. Among all the machine learning models, convolutional neural network (CNN) is one of the most studied and validated artificial neural networks in image recognition. Not only that it has great performance, but the design of most modern CNN hidden layers also allows the model to extract meaningful features without the needs of prior knowledge. Thus, the pathology community is showing increasing interests in comparing CNN to human judgments. As demonstrated in a number of studies reporting various image analysis models that can accurately localize and characterize cells into different cell types and predict patient outcome, the pathological field is incorporating artificial intelligence technologies into their diagnosis. Although using the deep neural network on recognizing pathological slides is not a new idea and is showing promising results, its requirement of a large quantity of data for training can be a big obstacle for many unpopular histopathological cases. In the bladder cancer field, the Tumor-Node-Metastasis (TNM) system defines T1 bladder cancer as the invasion of tumor cells into the lamina propria (LP). However, pathologists often struggle to confirm LP and/or muscularis mucosae invasion using hematoxylin & eosin (H&E) stains from bladder biopsies. Accurately reporting the presence of tumor invasion, which is associated with worse clinical outcomes, is critical for adequate patient management. In this thesis, we have developed various traditional machine learning models and compared their performances to 2 convolutional neural networks (CNN), VGG16 and VGG19, on histology image classification in distinguishing non-invasive versus invasive bladder tumors. By using approximately 1,200 H&E images from non-invasive and invasive bladder cancer tissues, our results showed the traditional machine learning methods with the human-directed features outperformed the fully automated CNN model as much as 12%. For 2-class classification task to distinguish non-invasive and invasive bladder cancer tissues, we achieved around 91~96% accuracy by using classic machine learning classifiers such as random forest, logistic regression, and probabilistic neural network. Whereas, CNN with VGG16 as hidden layers only achieved around 84%. In addition to performance, because of the transparency of features extraction in the pipeline, we were able to evaluate and rank the patterns in the bladder histological images. As based on their relative importance in prediction, classic machine learning methods provided a well-rounded approach under limited data size. Advisors/Committee Members: Feng Cui.

Subjects/Keywords: Bladder cancer; Convolutional neural network; Histology; Invasive; Probailistic neural network; Tumor-node-metastasis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yin, P. N. (2019). Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10107

Chicago Manual of Style (16^th Edition):

Yin, Peng Nien. “Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed January 20, 2021. https://scholarworks.rit.edu/theses/10107.

MLA Handbook (7^th Edition):

Yin, Peng Nien. “Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network.” 2019. Web. 20 Jan 2021.

Vancouver:

Yin PN. Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Jan 20]. Available from: https://scholarworks.rit.edu/theses/10107.

Council of Science Editors:

Yin PN. Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10107

NSYSU

26. Wu, Tung-Han. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

► In this paper, we combine the deep neural network De-noising Auto Encoder and Gaussian Mixture Model to implement an automatic speech recognition system in the… (more)

Subjects/Keywords: speech recognition; denoising auto encoder; convolutional neural network; deep learning; fully connected neural network

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APA (6^th Edition):

Wu, T. (2017). Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wu, Tung-Han. “Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition.” 2017. Web. 20 Jan 2021.

Vancouver:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu T. Combined with Deep Neural Network De-noising Auto Encoder on Noise-Robust Digit Continuous Speech Recognition. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716117-130558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Texas

27. Hesamifard, Ehsan. Privacy Preserving Machine Learning as a Service.

Degree: 2020, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc1703277/

► Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires… (more)

Subjects/Keywords: Homomorphic encryption; machine learning; data privacy; deep learning; convolutional neural network; neural network

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APA (6^th Edition):

Hesamifard, E. (2020). Privacy Preserving Machine Learning as a Service. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1703277/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hesamifard, Ehsan. “Privacy Preserving Machine Learning as a Service.” 2020. Thesis, University of North Texas. Accessed January 20, 2021. https://digital.library.unt.edu/ark:/67531/metadc1703277/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hesamifard, Ehsan. “Privacy Preserving Machine Learning as a Service.” 2020. Web. 20 Jan 2021.

Vancouver:

Hesamifard E. Privacy Preserving Machine Learning as a Service. [Internet] [Thesis]. University of North Texas; 2020. [cited 2021 Jan 20]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1703277/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hesamifard E. Privacy Preserving Machine Learning as a Service. [Thesis]. University of North Texas; 2020. Available from: https://digital.library.unt.edu/ark:/67531/metadc1703277/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

28. Wu, Pei-Hsuan. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

► Deep Neural Networks (DNN) widely used in computer vision applications have superior performance in image classification and object detection. However, the huge amount of data… (more)

Subjects/Keywords: CNN hardware accelerator; deep neural network (DNN); convolutional neural network (CNN); machine learning

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APA (6^th Edition):

Wu, P. (2018). Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Thesis, NSYSU. Accessed January 20, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Web. 20 Jan 2021.

Vancouver:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Jan 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Southern University

29. Islam, Mohammad Anwarul. Reduced Dataset Neural Network Model for Manuscript Character Recognition.

Degree: MSin Mathematics (M.S.), Department of Mathematical Sciences, 2020, Georgia Southern University

URL: https://digitalcommons.georgiasouthern.edu/etd/2138

► The automatic character recognition task has been of practical interest for a long time. Nowadays, there are well-established technologies and software to perform character… (more)

Subjects/Keywords: Neural network; Convolution; Convolutional neural network; Resampling.; Other Applied Mathematics; Other Mathematics

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APA (6^th Edition):

Islam, M. A. (2020). Reduced Dataset Neural Network Model for Manuscript Character Recognition. (Masters Thesis). Georgia Southern University. Retrieved from https://digitalcommons.georgiasouthern.edu/etd/2138

Chicago Manual of Style (16^th Edition):

Islam, Mohammad Anwarul. “Reduced Dataset Neural Network Model for Manuscript Character Recognition.” 2020. Masters Thesis, Georgia Southern University. Accessed January 20, 2021. https://digitalcommons.georgiasouthern.edu/etd/2138.

MLA Handbook (7^th Edition):

Islam, Mohammad Anwarul. “Reduced Dataset Neural Network Model for Manuscript Character Recognition.” 2020. Web. 20 Jan 2021.

Vancouver:

Islam MA. Reduced Dataset Neural Network Model for Manuscript Character Recognition. [Internet] [Masters thesis]. Georgia Southern University; 2020. [cited 2021 Jan 20]. Available from: https://digitalcommons.georgiasouthern.edu/etd/2138.

Council of Science Editors:

Islam MA. Reduced Dataset Neural Network Model for Manuscript Character Recognition. [Masters Thesis]. Georgia Southern University; 2020. Available from: https://digitalcommons.georgiasouthern.edu/etd/2138

University of Bridgeport

30. Hassan, Abdalraouf. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .

Degree: 2018, University of Bridgeport

URL: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

► The evolution of the social media and the e-commerce sites produces a massive amount of unstructured text data on the internet. Thus, there is a… (more)

▼ The evolution of the social media and the e-commerce sites produces a massive amount of unstructured text data on the internet. Thus, there is a high demand to develop an intelligent model to process it and extract a useful information from it. Text classification plays an important task for many Natural Language Processing (NLP) applications such as, sentiment analysis, web search, spam filtering, and information retrieval, in which we need to assign single or multiple predefined categories to a sequence of text. In Neural Network Language Models learning long-term dependencies with gradient descent is difficult due to the vanishing gradient problem. Recently researchers started to increase the depth of the network in order to overcome the limitations of the existing techniques. However, increasing the depth of the network means increasing the number of the parameters, which makes the network computationally expensive, and more prone to overfitting. Furthermore, NLP systems traditionally treat words as discrete atomic symbols; the model can leverage small amounts of information regarding the relationship between the individual symbols. In recent years, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to language modeling with comparative, remarkable results. CNNs are a noble approach to extract higher-level features invariant to local translation. However, this method requires the stacking of multiple convolutional layers in order to capture long-term dependencies because of the locality of the convolutional and pooling layers. In this dissertation, we introduce a joint CNN-RNN framework to overcome the problems in the existing deep learning models. Briefly, we applied an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via Long-Short-Term Memory (LSTM). Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review (IMDB) dataset, and the Stanford Sentiment Treebank (SSTb) dataset. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer with no pooling layers. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.

Subjects/Keywords: Convolutional neural network; Deep learning; Machine learning; Natural language processing; Recurrent neural network; Sentiment analysis

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APA (6^th Edition):

Hassan, A. (2018). Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Thesis, University of Bridgeport. Accessed January 20, 2021. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hassan, Abdalraouf. “Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks .” 2018. Web. 20 Jan 2021.

Vancouver:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2021 Jan 20]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hassan A. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations