+publisher:"Delft University of Technology" +contributor:("Reinders, Marcel")

Delft University of Technology

1. Pourquié, Valérie (author). Dynamics of the transcriptome and proteome in regions of the brain differ considerably.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:38d4d9e6-f6a2-448a-9a31-c0fc3258a301

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Human brain research is advancing, facilitated by improved high-throughput techniques and systematically preservation of brain tissue in brain banks. Due to the complex organization and… (more)

Subjects/Keywords: Brain; Proteomics; Transcriptomics

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APA (6^th Edition):

Pourquié, V. (. (2019). Dynamics of the transcriptome and proteome in regions of the brain differ considerably. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:38d4d9e6-f6a2-448a-9a31-c0fc3258a301

Chicago Manual of Style (16^th Edition):

Pourquié, Valérie (author). “Dynamics of the transcriptome and proteome in regions of the brain differ considerably.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:38d4d9e6-f6a2-448a-9a31-c0fc3258a301.

MLA Handbook (7^th Edition):

Pourquié, Valérie (author). “Dynamics of the transcriptome and proteome in regions of the brain differ considerably.” 2019. Web. 27 Feb 2021.

Vancouver:

Pourquié V(. Dynamics of the transcriptome and proteome in regions of the brain differ considerably. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:38d4d9e6-f6a2-448a-9a31-c0fc3258a301.

Council of Science Editors:

Pourquié V(. Dynamics of the transcriptome and proteome in regions of the brain differ considerably. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:38d4d9e6-f6a2-448a-9a31-c0fc3258a301

Delft University of Technology

2. Thomaidou, Eftychia (author). Inferring features from 5'UTR sequences to Translation Initiation Rates in S.cerevisiae.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:dbc6962c-d4d5-4dcb-b6d6-e832bfa05ad6

► In this research, we studied the impact of the 5'UTR sequences on translation. This is done by generating various features describing the 5'UTR. Those features… (more)

Subjects/Keywords: bioinformatics; Machine Learning; translation initiation rates; S.cerevisiae

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APA (6^th Edition):

Thomaidou, E. (. (2017). Inferring features from 5'UTR sequences to Translation Initiation Rates in S.cerevisiae. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dbc6962c-d4d5-4dcb-b6d6-e832bfa05ad6

Chicago Manual of Style (16^th Edition):

Thomaidou, Eftychia (author). “Inferring features from 5'UTR sequences to Translation Initiation Rates in S.cerevisiae.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:dbc6962c-d4d5-4dcb-b6d6-e832bfa05ad6.

MLA Handbook (7^th Edition):

Thomaidou, Eftychia (author). “Inferring features from 5'UTR sequences to Translation Initiation Rates in S.cerevisiae.” 2017. Web. 27 Feb 2021.

Vancouver:

Thomaidou E(. Inferring features from 5'UTR sequences to Translation Initiation Rates in S.cerevisiae. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:dbc6962c-d4d5-4dcb-b6d6-e832bfa05ad6.

Council of Science Editors:

Thomaidou E(. Inferring features from 5'UTR sequences to Translation Initiation Rates in S.cerevisiae. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:dbc6962c-d4d5-4dcb-b6d6-e832bfa05ad6

Delft University of Technology

3. Michielsen, Lieke (author). Automatic cell identification in single-cell RNA-sequencing data.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:a7a2a1f7-486e-47bb-afb8-7cdc891db795

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Since the revolution of single-cell RNA-sequencing, the number of available datasets has increased enormously. In these datasets, cell identification is mainly done manually, which is… (more)

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Since the revolution of single-cell RNA-sequencing, the number of available datasets has increased enormously. In these datasets, cell identification is mainly done manually, which is subjective and time-consuming. As a consequence, most datasets are annotated at a different resolution. This is not surprising as cell types form a hierarchy, but it can be problematic for downstream analysis or comparison of datasets. Several supervised methods have already been developed to overcome the drawbacks of unsupervised learning. None of these, however, combines the information found in multiple datasets and preserves the definition of cell populations in each dataset, while this consistency is necessary for downstream analysis. Furthermore, a supervised classifier should be able to detect new cell populations in an unlabeled dataset. Here, we introduce a hierarchical progressive learning pipeline with a one-class classifier to face these challenges. Using this pipeline, it is possible to construct a hierarchical classification tree by combining the information of multiple datasets. If datasets are annotated at a different resolution, their cell populations will be at different levels in the tree and all definitions are thus preserved. By using a one-class classifier for each cell population it is also possible to have a correctly working rejection option and discover new cell populations. In this paper, we show that it is possible to construct a classification tree for simulated data and immune cells. When comparing the pipeline with a one-class to a linear classifier, we show that a one-class classifier can indeed improve the rejection option. Using a linear classifier, on the other hand, results in a higher accuracy. Choosing between a one-class and a linear classifier is a trade-off between the ability of discovering new cell populations and a higher performance.

Computer Science | Data Science and Technology | Bioinformatics

Advisors/Committee Members: Reinders, Marcel (mentor), Mahfouz, Ahmed (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Cell types; Transcriptomics; Machine learning

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APA (6^th Edition):

Michielsen, L. (. (2020). Automatic cell identification in single-cell RNA-sequencing data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a7a2a1f7-486e-47bb-afb8-7cdc891db795

Chicago Manual of Style (16^th Edition):

Michielsen, Lieke (author). “Automatic cell identification in single-cell RNA-sequencing data.” 2020. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:a7a2a1f7-486e-47bb-afb8-7cdc891db795.

MLA Handbook (7^th Edition):

Michielsen, Lieke (author). “Automatic cell identification in single-cell RNA-sequencing data.” 2020. Web. 27 Feb 2021.

Vancouver:

Michielsen L(. Automatic cell identification in single-cell RNA-sequencing data. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:a7a2a1f7-486e-47bb-afb8-7cdc891db795.

Council of Science Editors:

Michielsen L(. Automatic cell identification in single-cell RNA-sequencing data. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:a7a2a1f7-486e-47bb-afb8-7cdc891db795

Delft University of Technology

4. Klip, Roy (author). Fuzzy Face Clustering For Forensic Investigations.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:a9f82787-ac3d-4ff1-8239-4f3c1c6414b9

► The amount of personal imagery kept on (mobile) devices is increasing by the day. Analysis and organization of these large collections of data are becoming… (more)

Subjects/Keywords: Face Clustering; Deep Learning; Fuzzy Clustering

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APA (6^th Edition):

Klip, R. (. (2019). Fuzzy Face Clustering For Forensic Investigations. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a9f82787-ac3d-4ff1-8239-4f3c1c6414b9

Chicago Manual of Style (16^th Edition):

Klip, Roy (author). “Fuzzy Face Clustering For Forensic Investigations.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:a9f82787-ac3d-4ff1-8239-4f3c1c6414b9.

MLA Handbook (7^th Edition):

Klip, Roy (author). “Fuzzy Face Clustering For Forensic Investigations.” 2019. Web. 27 Feb 2021.

Vancouver:

Klip R(. Fuzzy Face Clustering For Forensic Investigations. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:a9f82787-ac3d-4ff1-8239-4f3c1c6414b9.

Council of Science Editors:

Klip R(. Fuzzy Face Clustering For Forensic Investigations. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a9f82787-ac3d-4ff1-8239-4f3c1c6414b9

Delft University of Technology

5. Lengyel, Attila (author). Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a

► This work investigates how prior knowledge from physics-based reflection models can be used to improve the performance of semantic segmentation models under an illumination-based domain… (more)

Subjects/Keywords: Semantic segmentation; color invariants; deep learning; computer vision; domain adaptation

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APA (6^th Edition):

Lengyel, A. (. (2019). Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a

Chicago Manual of Style (16^th Edition):

Lengyel, Attila (author). “Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a.

MLA Handbook (7^th Edition):

Lengyel, Attila (author). “Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.” 2019. Web. 27 Feb 2021.

Vancouver:

Lengyel A(. Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a.

Council of Science Editors:

Lengyel A(. Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a

Delft University of Technology

6. Elghlan, Faris (author). One-Class Classification: for high-dimensional data.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:b5aabe84-8a8b-4841-848d-136ab6ce0825

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This M.Sc. thesis report investigates the application of one-class classification techniques to complex high-dimensional data. The aim of a one-class classifier is to separate target… (more)

Subjects/Keywords: one-class; classification; high-dimensional; Autoencoder; GAN; Wasserstein Autoencoder; Pattern Recognition; Machine Learning; Deep Learning

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APA (6^th Edition):

Elghlan, F. (. (2019). One-Class Classification: for high-dimensional data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b5aabe84-8a8b-4841-848d-136ab6ce0825

Chicago Manual of Style (16^th Edition):

Elghlan, Faris (author). “One-Class Classification: for high-dimensional data.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:b5aabe84-8a8b-4841-848d-136ab6ce0825.

MLA Handbook (7^th Edition):

Elghlan, Faris (author). “One-Class Classification: for high-dimensional data.” 2019. Web. 27 Feb 2021.

Vancouver:

Elghlan F(. One-Class Classification: for high-dimensional data. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:b5aabe84-8a8b-4841-848d-136ab6ce0825.

Council of Science Editors:

Elghlan F(. One-Class Classification: for high-dimensional data. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:b5aabe84-8a8b-4841-848d-136ab6ce0825

Delft University of Technology

7. Pathak, Chinmay (author). Exploring normalizing flow for anomaly detection.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852

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Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network… (more)

Subjects/Keywords: Anomaly Detection; Outlier detection; Autoencoder; Generative Algorithms; unsupervised learning; one-class classification; GLOW; Normalizing flows

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

APA (6^th Edition):

Pathak, C. (. (2019). Exploring normalizing flow for anomaly detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852

Chicago Manual of Style (16^th Edition):

Pathak, Chinmay (author). “Exploring normalizing flow for anomaly detection.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852.

MLA Handbook (7^th Edition):

Pathak, Chinmay (author). “Exploring normalizing flow for anomaly detection.” 2019. Web. 27 Feb 2021.

Vancouver:

Pathak C(. Exploring normalizing flow for anomaly detection. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852.

Council of Science Editors:

Pathak C(. Exploring normalizing flow for anomaly detection. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852

Delft University of Technology

8. Garbacz, Mateusz (author). Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:13d6eb0e-4b34-4a41-86a8-9cbab66ecfa0

► Being capable to foresee the future of a given financial asset as an investor, may lead to significant economic profits. Therefore, stock market prediction is… (more)

▼ Being capable to foresee the future of a given financial asset as an investor, may lead to significant economic profits. Therefore, stock market prediction is a field that has been extensively developed by numerous researchers and companies. Recently, however, a new branch of financial assets has emerged, namely cryptocurrencies. As a representative of these tokens, we chose the largest and most popular cryptocurrency, called Bitcoin. Its value is characterized by with non-stationary behaviour and occurrence of speculative bubbles, which cause a rapid explosion of the price, followed by a major crisis and market panic. Currently, most of the research community does not take these issues into account, while predicting its price, which may lead to wrong conclusions or unstable results. Therefore, in this thesis, we take a step back and reconsider how does the environment influence model's performance and how to use this knowledge to implement more accurate forecast in the future. Moreover, by designing an appropriate methodology and employing semantic features from online text sources, such as Twitter, Reddit and online news portals, we attempt to build a robust prediction system that offers stable performance regardless of the market fluctuations. Executed experiments prove that non-stationarity negatively influences the results, causing the deterioration of model's performance over time. Furthermore, it appears that there may be certain properties of economic bubbles that facilitate more efficient prediction, as well as some predictors have an ability to successfully forecast the beginning of a market crisis. However, these findings are based on individual observations, which need to be confirmed by further research. In addition, by designing an appropriate methodology, we prevented performance deterioration, caused by price signal non-stationarity. Although, the semantic features based on online sources did not boost the robustness of the system significantly, combined with the suitable system's design, they lead to improvement in the overall performance of the predictor. Advisors/Committee Members: Loog, Marco (mentor), Reinders, Marcel (graduation committee), Bozzon, Alessandro (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Bitcoin; Bubble; Prediction; Machine Learning; Deep Learning; Non-stationarity

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APA (6^th Edition):

Garbacz, M. (. (2018). Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:13d6eb0e-4b34-4a41-86a8-9cbab66ecfa0

Chicago Manual of Style (16^th Edition):

Garbacz, Mateusz (author). “Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:13d6eb0e-4b34-4a41-86a8-9cbab66ecfa0.

MLA Handbook (7^th Edition):

Garbacz, Mateusz (author). “Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction.” 2018. Web. 27 Feb 2021.

Vancouver:

Garbacz M(. Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:13d6eb0e-4b34-4a41-86a8-9cbab66ecfa0.

Council of Science Editors:

Garbacz M(. Time series forecast in non-stationary environment with occurrence of economic bubbles: Bitcoin Price prediction. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:13d6eb0e-4b34-4a41-86a8-9cbab66ecfa0

Delft University of Technology

9. Lelekas, Ioannis (author). Top-Down Networks: A coarse-to-fine reimagination of CNNs.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

► Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Top-Down; Fine-to-Coarse; Coarse-to-Fine; Adversarial attacks; Adversarial robustness; Gradcam; Object localization

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APA (6^th Edition):

Lelekas, I. (. (2020). Top-Down Networks: A coarse-to-fine reimagination of CNNs. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

Chicago Manual of Style (16^th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

MLA Handbook (7^th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Web. 27 Feb 2021.

Vancouver:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

Council of Science Editors:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

Delft University of Technology

10. Li, Yadong (author). Quantitative evaluation of Generative Adversarial Networks and improved training techniques.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:b01dcd2b-fdbd-4531-bf1b-0c0b2881df48

► Generative adversarial networks (GANs) are a class of generative models, for which the goal is to learn from training data and then to generate data… (more)

Subjects/Keywords: Generative Adversarial Networks; Quantitative Evaluation; Wasserstein GANs

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APA (6^th Edition):

Li, Y. (. (2018). Quantitative evaluation of Generative Adversarial Networks and improved training techniques. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b01dcd2b-fdbd-4531-bf1b-0c0b2881df48

Chicago Manual of Style (16^th Edition):

Li, Yadong (author). “Quantitative evaluation of Generative Adversarial Networks and improved training techniques.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:b01dcd2b-fdbd-4531-bf1b-0c0b2881df48.

MLA Handbook (7^th Edition):

Li, Yadong (author). “Quantitative evaluation of Generative Adversarial Networks and improved training techniques.” 2018. Web. 27 Feb 2021.

Vancouver:

Li Y(. Quantitative evaluation of Generative Adversarial Networks and improved training techniques. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:b01dcd2b-fdbd-4531-bf1b-0c0b2881df48.

Council of Science Editors:

Li Y(. Quantitative evaluation of Generative Adversarial Networks and improved training techniques. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:b01dcd2b-fdbd-4531-bf1b-0c0b2881df48

Delft University of Technology

11. van Doorn, Felix (author). Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:b13e7c6e-03ee-43b2-8b0a-6b79c5e31e5b

► In this thesis, leaving behaviour in a small group setting is studied. In the past, group conversations have mostly been studied in a static setting.… (more)

▼ In this thesis, leaving behaviour in a small group setting is studied. In the past, group conversations have mostly been studied in a static setting. Inclusion of a temporal dimension would be of use in numerous applications. In order to account for temporal elements, we must understand how group conversations evolve. Leaving and joining a conversation are a vital part of social interaction and the driving factors behind the evolution of groups. Understanding group evolution has numerous applications, such as (social) surveillance, socially intelligent robotics and multimedia content analysis. In this thesis, I will focus on researching the nature of leaving behaviour in a mingling setting. A better understanding of leaving behaviour is required to incorporate this behaviour in models of group evolution. By performing an ethnographic study on video material from the MatchNMingle dataset, 90 minutes of annotations of conversational leaving behaviour are collected. These annotations are subsequently combined with action annotations (either ground truth and automatically extracted) and accelerometer readings from the same dataset to describe the behaviour in each interaction. Using a sliding window approach, subsequences of these interactions are extracted using window sizes between 10 and 30 seconds. From these subsequences, pairwise coordination features are extracted between each pair present in the group. These coordination behaviours are known to be indicative of conversational involvement, which is hypothesized to be linked to leaving. These features are used in two different experimental settings. In the first of these settings, a classifier was trained on subsequences of these interactions. This classifier attempts to predict whether a sequence of interaction with result in a leave within the next n seconds, where n varies between 0 and 30 seconds. In the second experiments, the classifier is replaced with a regression model. For each of the determined subsequences, the time until the interaction ends is assigned and fit the regression model on these inputs. This is the first study that has attempted to predict leaving using these data sources. Using this approach, above random performance was achieved. In the classification setting an AUC of 0.63 +/- 0.12 is achieved, which is significantly better than random performance. In the regression experiments no satisfactory results were obtained. From these results we can conclude that it is possible to predict conversational leaving behaviour from coordination features. Although there are many possible improvements, the proposed methodology can serve as solid foundation for future research. This does not only include the proposed methodology for creation of predictive models, but also the new feature sets on which these experiments were conducted. Advisors/Committee Members: Hung, Hayley (mentor), Reinders, Marcel (graduation committee), Liem, Cynthia (graduation committee), Delft University of Technology (degree granting institution).

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

APA (6^th Edition):

van Doorn, F. (. (2018). Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b13e7c6e-03ee-43b2-8b0a-6b79c5e31e5b

Chicago Manual of Style (16^th Edition):

van Doorn, Felix (author). “Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:b13e7c6e-03ee-43b2-8b0a-6b79c5e31e5b.

MLA Handbook (7^th Edition):

van Doorn, Felix (author). “Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation.” 2018. Web. 27 Feb 2021.

Vancouver:

van Doorn F(. Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:b13e7c6e-03ee-43b2-8b0a-6b79c5e31e5b.

Council of Science Editors:

van Doorn F(. Rituals of Leaving: Predictive Modelling of Leaving Behaviour in Conversation. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:b13e7c6e-03ee-43b2-8b0a-6b79c5e31e5b

Delft University of Technology

12. Uijens, Wouter (author). Activating frequencies: Exploring non-linearities in the Fourier domain.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf

► Convolutional Neural Networks (CNNs) are achieving state of the art performance in computer vision. One downside of CNNs is their computational complexity. One way to… (more)

Subjects/Keywords: Machine Learning; Fourier Transform; Activation Function

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APA (6^th Edition):

Uijens, W. (. (2018). Activating frequencies: Exploring non-linearities in the Fourier domain. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf

Chicago Manual of Style (16^th Edition):

Uijens, Wouter (author). “Activating frequencies: Exploring non-linearities in the Fourier domain.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf.

MLA Handbook (7^th Edition):

Uijens, Wouter (author). “Activating frequencies: Exploring non-linearities in the Fourier domain.” 2018. Web. 27 Feb 2021.

Vancouver:

Uijens W(. Activating frequencies: Exploring non-linearities in the Fourier domain. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf.

Council of Science Editors:

Uijens W(. Activating frequencies: Exploring non-linearities in the Fourier domain. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf

Delft University of Technology

13. Wen, Xiaoming (author). Learning Scale-Aware Optical Flow.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c

► Optical flow is a representation of projected real-world motion of the object between two consecutive images. The optical flow measures the pixel displacement on the… (more)

Subjects/Keywords: Optica Flow; CNN; Scale-Aware; Derivative

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APA (6^th Edition):

Wen, X. (. (2018). Learning Scale-Aware Optical Flow. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c

Chicago Manual of Style (16^th Edition):

Wen, Xiaoming (author). “Learning Scale-Aware Optical Flow.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c.

MLA Handbook (7^th Edition):

Wen, Xiaoming (author). “Learning Scale-Aware Optical Flow.” 2018. Web. 27 Feb 2021.

Vancouver:

Wen X(. Learning Scale-Aware Optical Flow. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c.

Council of Science Editors:

Wen X(. Learning Scale-Aware Optical Flow. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c

Delft University of Technology

14. Priadi Teguh Wibowo, Priadi (author). Automatic Running Event Visualization using Video from Multiple Camera.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174

►

Visualizing runners trajectory from video data is not straightforward because the video data does not contain the explicit information of which runners appear in the… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Visualization; Person Re-identification; Scene Text Recognition

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APA (6^th Edition):

Priadi Teguh Wibowo, P. (. (2019). Automatic Running Event Visualization using Video from Multiple Camera. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174

Chicago Manual of Style (16^th Edition):

Priadi Teguh Wibowo, Priadi (author). “Automatic Running Event Visualization using Video from Multiple Camera.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174.

MLA Handbook (7^th Edition):

Priadi Teguh Wibowo, Priadi (author). “Automatic Running Event Visualization using Video from Multiple Camera.” 2019. Web. 27 Feb 2021.

Vancouver:

Priadi Teguh Wibowo P(. Automatic Running Event Visualization using Video from Multiple Camera. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174.

Council of Science Editors:

Priadi Teguh Wibowo P(. Automatic Running Event Visualization using Video from Multiple Camera. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174

Delft University of Technology

15. Liu, Lu (author). People Detection from Overhead Cameras: A study of impact of occlusion on performance.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:33a6b9b6-f26c-4ef1-8047-5c33d95487c6

►

During the last decades, people detection has received great attention in computer vision and pattern recognition because of its various applications. Though there are thousands… (more)

Subjects/Keywords: People Detection; Occlusion; Deep Learning

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APA (6^th Edition):

Liu, L. (. (2018). People Detection from Overhead Cameras: A study of impact of occlusion on performance. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:33a6b9b6-f26c-4ef1-8047-5c33d95487c6

Chicago Manual of Style (16^th Edition):

Liu, Lu (author). “People Detection from Overhead Cameras: A study of impact of occlusion on performance.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:33a6b9b6-f26c-4ef1-8047-5c33d95487c6.

MLA Handbook (7^th Edition):

Liu, Lu (author). “People Detection from Overhead Cameras: A study of impact of occlusion on performance.” 2018. Web. 27 Feb 2021.

Vancouver:

Liu L(. People Detection from Overhead Cameras: A study of impact of occlusion on performance. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:33a6b9b6-f26c-4ef1-8047-5c33d95487c6.

Council of Science Editors:

Liu L(. People Detection from Overhead Cameras: A study of impact of occlusion on performance. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:33a6b9b6-f26c-4ef1-8047-5c33d95487c6

Delft University of Technology

16. Starre, Rolf (author). Action Selection Policies for Walking Monte Carlo Tree Search.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:3947ef53-eab3-46a2-9efc-fff985cd96c9

► Recent Reinforcement Learning methods have combined function approximation and Monte Carlo Tree Search and are able to learn by self-play up to a very high… (more)

Subjects/Keywords: Monte Carlo Tree Search; Reinforcement Learning; Exploration; Action selection policies

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APA (6^th Edition):

Starre, R. (. (2018). Action Selection Policies for Walking Monte Carlo Tree Search. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3947ef53-eab3-46a2-9efc-fff985cd96c9

Chicago Manual of Style (16^th Edition):

Starre, Rolf (author). “Action Selection Policies for Walking Monte Carlo Tree Search.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:3947ef53-eab3-46a2-9efc-fff985cd96c9.

MLA Handbook (7^th Edition):

Starre, Rolf (author). “Action Selection Policies for Walking Monte Carlo Tree Search.” 2018. Web. 27 Feb 2021.

Vancouver:

Starre R(. Action Selection Policies for Walking Monte Carlo Tree Search. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:3947ef53-eab3-46a2-9efc-fff985cd96c9.

Council of Science Editors:

Starre R(. Action Selection Policies for Walking Monte Carlo Tree Search. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:3947ef53-eab3-46a2-9efc-fff985cd96c9

Delft University of Technology

17. Kolthof, Daan (author). Recognizing and Handling Negations in Machine Learning.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:caa1b4b3-59ca-4290-b95e-84190c54b787

► In several machine learning problems, a relatively small subproblem is present in which combinations of (negating) objects or structures result in a negation or otherwise… (more)

Subjects/Keywords: Machine Learning; Neural Networks; Natural Language Processing; sentiment analysis; sentiment classification; Word embedding; Negation Handling; Negation Recognition

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APA (6^th Edition):

Kolthof, D. (. (2018). Recognizing and Handling Negations in Machine Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:caa1b4b3-59ca-4290-b95e-84190c54b787

Chicago Manual of Style (16^th Edition):

Kolthof, Daan (author). “Recognizing and Handling Negations in Machine Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:caa1b4b3-59ca-4290-b95e-84190c54b787.

MLA Handbook (7^th Edition):

Kolthof, Daan (author). “Recognizing and Handling Negations in Machine Learning.” 2018. Web. 27 Feb 2021.

Vancouver:

Kolthof D(. Recognizing and Handling Negations in Machine Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:caa1b4b3-59ca-4290-b95e-84190c54b787.

Council of Science Editors:

Kolthof D(. Recognizing and Handling Negations in Machine Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:caa1b4b3-59ca-4290-b95e-84190c54b787

Delft University of Technology

18. Mandersloot, Jeroen (author). Model-based rare category detection for temporal data.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4

► Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which… (more)

Subjects/Keywords: rare category detection; temporal data; semi-supervised learning; mixture models; markov random fields

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APA (6^th Edition):

Mandersloot, J. (. (2018). Model-based rare category detection for temporal data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4

Chicago Manual of Style (16^th Edition):

Mandersloot, Jeroen (author). “Model-based rare category detection for temporal data.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4.

MLA Handbook (7^th Edition):

Mandersloot, Jeroen (author). “Model-based rare category detection for temporal data.” 2018. Web. 27 Feb 2021.

Vancouver:

Mandersloot J(. Model-based rare category detection for temporal data. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4.

Council of Science Editors:

Mandersloot J(. Model-based rare category detection for temporal data. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4

Delft University of Technology

19. Adhikari, Ajaya (author). Example and Feature importance-based Explanations for Black-box Machine Learning Models.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:f8f9df3e-7668-418d-9dd2-92f4023e2187

► Machine Learning (ML) is a rapidly growing field. There has been a surge of complex black-box models with high performance. On the other hand, the… (more)

Subjects/Keywords: example-based explanation; explainable machine Learning; contrastive explanation; feature importance explanation; leafage

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APA (6^th Edition):

Adhikari, A. (. (2018). Example and Feature importance-based Explanations for Black-box Machine Learning Models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f8f9df3e-7668-418d-9dd2-92f4023e2187

Chicago Manual of Style (16^th Edition):

Adhikari, Ajaya (author). “Example and Feature importance-based Explanations for Black-box Machine Learning Models.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:f8f9df3e-7668-418d-9dd2-92f4023e2187.

MLA Handbook (7^th Edition):

Adhikari, Ajaya (author). “Example and Feature importance-based Explanations for Black-box Machine Learning Models.” 2018. Web. 27 Feb 2021.

Vancouver:

Adhikari A(. Example and Feature importance-based Explanations for Black-box Machine Learning Models. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:f8f9df3e-7668-418d-9dd2-92f4023e2187.

Council of Science Editors:

Adhikari A(. Example and Feature importance-based Explanations for Black-box Machine Learning Models. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:f8f9df3e-7668-418d-9dd2-92f4023e2187

Delft University of Technology

20. Brand, Patrick (author). Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:9917401d-c38d-4ad8-9d5c-75657058c3e6

►

Recent advances in Artificial Intelligence and Computer Vision have been showed to be promising for automated land use classification of remotely sensed data. However, current… (more)

▼

Recent advances in Artificial Intelligence and Computer Vision have been showed to be promising for automated land use classification of remotely sensed data. However, current state-of-the-art per-pixel segmentation networks fail to accurately capture geometrical and topological properties on land use segmentation, as these methods have inherently a lot of freedom. These geometrical and topological properties of land use structures are crucial for describing land usage on topographical maps, as their purpose is to present insight into topology and borders of land use structures. In order to preserve the geometrical and topological properties of land use structures, a novel segmentation method is introduced and tested on road structures in this thesis. Unlike current state-of-the-art segmentation networks, this new method performs the segmentation task by utilizing shape regression techniques as currently applied by state-of-the-art object detection networks. As modern object detection methods are only able to perform regression on simplistic shapes, and road structures generally describe complex shapes, a new topology preserving annotation generation method is introduced that subdivides a complex road structure into a set of oriented rectangular shapes. Since not many publicly available land use datasets contain both aerial images and per-pixel annotations, a new dataset based on aerial images and land use annotations, covering large areas of the Netherlands, is introduced as well. The results show that the novel segmentation method is capable of learning the newly introduced road structure representation, which preserves geometrical and topological properties. The connectedness property, however, is lost. The novel method does currently not outperform current state-of-the-art per-pixel segmentation networks, although several directions for future work are proposed to improve the segmentation performance of the shape regression based technique and preserve the connectedness property.

Computer Science

Advisors/Committee Members: Reinders, Marcel (graduation committee), Kooij, Julian (mentor), van der Maas, Daniel (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Computer vision; Deep learning; Remote sensing; Semantic segmentation; Shape regression; Land use

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

APA (6^th Edition):

Brand, P. (. (2019). Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9917401d-c38d-4ad8-9d5c-75657058c3e6

Chicago Manual of Style (16^th Edition):

Brand, Patrick (author). “Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:9917401d-c38d-4ad8-9d5c-75657058c3e6.

MLA Handbook (7^th Edition):

Brand, Patrick (author). “Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression.” 2019. Web. 27 Feb 2021.

Vancouver:

Brand P(. Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:9917401d-c38d-4ad8-9d5c-75657058c3e6.

Council of Science Editors:

Brand P(. Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9917401d-c38d-4ad8-9d5c-75657058c3e6

Delft University of Technology

21. Liu, Xin (author). Unsupervised Cross Domain Image Matching with Outlier Detection.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73

►

This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the… (more)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Outlier Detection

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

APA (6^th Edition):

Liu, X. (. (2018). Unsupervised Cross Domain Image Matching with Outlier Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73

Chicago Manual of Style (16^th Edition):

Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.

MLA Handbook (7^th Edition):

Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Web. 27 Feb 2021.

Vancouver:

Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.

Council of Science Editors:

Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73

Delft University of Technology

22. Li, Jiahui (author). Attention-Aware Age-Agnostic Visual Place Recognition.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7

► A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is… (more)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Attention Mechanism

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

APA (6^th Edition):

Li, J. (. (2019). Attention-Aware Age-Agnostic Visual Place Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7

Chicago Manual of Style (16^th Edition):

Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.

MLA Handbook (7^th Edition):

Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Web. 27 Feb 2021.

Vancouver:

Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.

Council of Science Editors:

Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7

Delft University of Technology

23. Dhar, Aniket (author). Rotation invariant filters in CNNs: applied to segmentation of aerial images for land-use classification.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:1624a31f-7976-425a-a2b6-d6937cc39895

►

Convolutional neural networks are showing incredible performance in image classification, segmentation, object detection and other computer vision applications in recent years. But they lack understanding… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Machine Learning; Convolutional Neural Networks

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

APA (6^th Edition):

Dhar, A. (. (2018). Rotation invariant filters in CNNs: applied to segmentation of aerial images for land-use classification. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1624a31f-7976-425a-a2b6-d6937cc39895

Chicago Manual of Style (16^th Edition):

Dhar, Aniket (author). “Rotation invariant filters in CNNs: applied to segmentation of aerial images for land-use classification.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:1624a31f-7976-425a-a2b6-d6937cc39895.

MLA Handbook (7^th Edition):

Dhar, Aniket (author). “Rotation invariant filters in CNNs: applied to segmentation of aerial images for land-use classification.” 2018. Web. 27 Feb 2021.

Vancouver:

Dhar A(. Rotation invariant filters in CNNs: applied to segmentation of aerial images for land-use classification. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:1624a31f-7976-425a-a2b6-d6937cc39895.

Council of Science Editors:

Dhar A(. Rotation invariant filters in CNNs: applied to segmentation of aerial images for land-use classification. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:1624a31f-7976-425a-a2b6-d6937cc39895

Delft University of Technology

24. van Bekhoven, Sjoerd (author). Predicting voluntary employee turnover using core employee data.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:95411815-890a-4aba-b6e0-336c9080cfc0

► Voluntary employee turnover is the process of an employee voluntarily choosing to resign from a company. High voluntary turnover has been shown to have negative… (more)

Subjects/Keywords: voluntary employee turnover; people analytics; binary classification; feature design; class imbalance; class overlap; feature importance

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APA (6^th Edition):

van Bekhoven, S. (. (2017). Predicting voluntary employee turnover using core employee data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:95411815-890a-4aba-b6e0-336c9080cfc0

Chicago Manual of Style (16^th Edition):

van Bekhoven, Sjoerd (author). “Predicting voluntary employee turnover using core employee data.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:95411815-890a-4aba-b6e0-336c9080cfc0.

MLA Handbook (7^th Edition):

van Bekhoven, Sjoerd (author). “Predicting voluntary employee turnover using core employee data.” 2017. Web. 27 Feb 2021.

Vancouver:

van Bekhoven S(. Predicting voluntary employee turnover using core employee data. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:95411815-890a-4aba-b6e0-336c9080cfc0.

Council of Science Editors:

van Bekhoven S(. Predicting voluntary employee turnover using core employee data. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:95411815-890a-4aba-b6e0-336c9080cfc0

Delft University of Technology

25. van Garderen, Karin (author). Active Learning for Overlay Prediction in Semi-conductor Manufacturing.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66

► In the manufacturing of semi-conductor devices there is a constant demand for increasing precision and yield. Measuring and controlling overlay errors is essential in this… (more)

Subjects/Keywords: Active Learning; Regression; Visualization

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

APA (6^th Edition):

van Garderen, K. (. (2018). Active Learning for Overlay Prediction in Semi-conductor Manufacturing. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66

Chicago Manual of Style (16^th Edition):

van Garderen, Karin (author). “Active Learning for Overlay Prediction in Semi-conductor Manufacturing.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66.

MLA Handbook (7^th Edition):

van Garderen, Karin (author). “Active Learning for Overlay Prediction in Semi-conductor Manufacturing.” 2018. Web. 27 Feb 2021.

Vancouver:

van Garderen K(. Active Learning for Overlay Prediction in Semi-conductor Manufacturing. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66.

Council of Science Editors:

van Garderen K(. Active Learning for Overlay Prediction in Semi-conductor Manufacturing. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:21b8d90a-a30c-49ea-8ef3-6dc98da25b66

Delft University of Technology

26. Hulsebos, Madelon (author). Outlier detection in multivariate time series: Exploiting reconstructions from random projections.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:0deb1a07-6b09-47e5-970a-b192eaea9591

Subjects/Keywords: online learning; outlier detection; unsupervised learning; multivariate time series; random projections

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):

Hulsebos, M. (. (2018). Outlier detection in multivariate time series: Exploiting reconstructions from random projections. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0deb1a07-6b09-47e5-970a-b192eaea9591

Chicago Manual of Style (16^th Edition):

Hulsebos, Madelon (author). “Outlier detection in multivariate time series: Exploiting reconstructions from random projections.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:0deb1a07-6b09-47e5-970a-b192eaea9591.

MLA Handbook (7^th Edition):

Hulsebos, Madelon (author). “Outlier detection in multivariate time series: Exploiting reconstructions from random projections.” 2018. Web. 27 Feb 2021.

Vancouver:

Hulsebos M(. Outlier detection in multivariate time series: Exploiting reconstructions from random projections. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:0deb1a07-6b09-47e5-970a-b192eaea9591.

Council of Science Editors:

Hulsebos M(. Outlier detection in multivariate time series: Exploiting reconstructions from random projections. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:0deb1a07-6b09-47e5-970a-b192eaea9591

Delft University of Technology

27. van Dorth, Matthijs (author). Probabilistic Models for Personalized Faceted Search.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:71486f25-5e91-4968-9a37-14907fee6481

Pattern Recognition & Bioinformatics Advisors/Committee Members: Reinders, Marcel (mentor), Loog, Marco (mentor), Liem, Cynthia (mentor), Kooij, Julian (mentor), Delft University of Technology (degree granting institution).

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):

van Dorth, M. (. (2017). Probabilistic Models for Personalized Faceted Search. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:71486f25-5e91-4968-9a37-14907fee6481

Chicago Manual of Style (16^th Edition):

van Dorth, Matthijs (author). “Probabilistic Models for Personalized Faceted Search.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:71486f25-5e91-4968-9a37-14907fee6481.

MLA Handbook (7^th Edition):

van Dorth, Matthijs (author). “Probabilistic Models for Personalized Faceted Search.” 2017. Web. 27 Feb 2021.

Vancouver:

van Dorth M(. Probabilistic Models for Personalized Faceted Search. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:71486f25-5e91-4968-9a37-14907fee6481.

Council of Science Editors:

van Dorth M(. Probabilistic Models for Personalized Faceted Search. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:71486f25-5e91-4968-9a37-14907fee6481

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Open Access Theses and Dissertations