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Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization with an Application to Prostate HDR Brachytherapy Automated Treatment Planning

Scholman, Renzo (author)

Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found approximation sets are not smoothly navigable because the solutions belong to various niches, which reduces the insight for...

master thesis 2022

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Determining epitope specificity of T-cell receptors with Transformers

Khan, Abdul Rehman (author)

Transformers have dominated the field of natural language processing due to their competency in learning complex relationships within a sequence. Reusing a pre-trained transformer for a downstream task is known as Trans-fer learning. Transfer learning restricts the transformer to a fixed vocabulary; modification in transformer implementation...

master thesis 2022

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Node Survivability and Dynamics of the Dutch Municipality Network

Manolopoulos, Ioannis (author)

This thesis’ research concerns the time-dynamics of a complex geographical network of municipalities, i.e. the Dutch Municipality Network over the period 1830-2019. By analysing 190 years of socioeconomic statistical data and applying contemporary tooling from network science and geographic information systems (GIS), the findings from this...

master thesis 2022

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Characterization and Mitigation of High-Confidence Errors Through the Use of Human-In-The-Loop Methods: Domain Expert Driven Approach to Model Development

Hoogland, Pavel (author)

In the use of Machine Learning systems, attaining the trust of those that are the end-users can often be difficult. Many of the current state-of-the-art systems operate as Black-Boxes. Errors produced by these Black-Box systems, without further explanation as to why these decisions were made, will deteriorate trust. This effect is especially...

master thesis 2022

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REAL Reinforcement Learning: Planning with adversarial models

Foffano, Daniele (author)

Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...

master thesis 2022

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The State of Data Streaming Practices at ING

Kanya Paramita Koesoemo, Kanya (author)

The development of data stream processing has become one of the key themes in the database and distributed system community throughout the world as data has grown on a large scale and in a range of industries over the last several years. Because data stream processing is a relatively new breakthrough in data-driven approaches, several teams at...

master thesis 2021

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Know what it does not know: Improving Offline Deep Reinforcement Learning with Uncertainty Estimation

Smit, Jordi (author)

Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...

master thesis 2021

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Exact Machine Learning: Improving Space and Speed of MaxSAT Solvers for Correlation Clustering

Marchal, Maxim (author)

Clustering is an important unsupervised learning task, with many applications in machine learning, computer vision, formal program verification and finance. Heuristic approaches such as local search are an excellent strategy for estimating the optimal solution, but they run the danger of getting stuck in local optima. Furthermore, it is non...

master thesis 2021

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Unsupervised Manifold Alignment with TopoGAN: Aligning multi-modal biological data without correspondence information available across modalities

Singh, Akash (author)

Single-cell multi-modal omics promises to open new doors in bioinformatics by measuring different aspects of cells, thus offering multiple perspectives on the underlying biological phenomenon. Although simultaneous multi-modal measurement protocols do exist, their inherent technical limitations necessitate focus on single modality measurements....

master thesis 2021

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Experience Sharing Conversational Agent for Type II Diabetes

Saravanan, Nash (author)

In this work, a conversational interaction was designed and implemented to test the effect of references to past events or shared experiences rephrased into motivational phrases within the context of working towards a diet related goal that can assist with type II diabetes over multiple sessions.<br/>Prior works that utilized a memory typically...

master thesis 2021

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Frequency learning for structured CNN filters with Gaussian fractional derivatives

Saldanha, Nikhil (author)

A structured CNN filter basis allows incorporating priors about natural image statistics and thus require less training examples to learn, saving valuable annotation time. Here, we build on the Gaussian derivative CNN filter basis that learn both the orientation and scale of the filters. However, this Gaussian filter basis definition depends on...

master thesis 2021

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Understanding Context Effects in the Evaluation of Music Similarity

van den Berg, Michiel (author)

This work analyses context effect in the evaluation of music similarity performed by human annotators to better understand the impact of context effects in the current annotation protocol of Music Information Retrieval Evaluation eXchange (MIREX). Human annotators are known to be subjective when giving similarity judgements. The Audio Music...

master thesis 2021

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Explainable artificial intelligence in forensics: realistic explanations for number of contributor predictions of DNA profiles

Veldhuis, Marthe (author)

Using machine learning to determine the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles has been shown to obtain good accuracy. However, the models used so far are not transparent to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of...

master thesis 2021

document

Reverse Engineering Relational Data for Entity Type Recognition in Enterprise Solutions at ING

Breurkes, Arthur (author)

Database entity type recognition is the practice of recognizing conceptual entity types for which given data sets contain data. In big data or data lake settings, it is not always known which conceptual entity types are represented in each data set, making it difficult to extract value from the data. Depending on the logical schemas, each...

master thesis 2021

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Generating Private Contrastive Explanations with Differentially Private Classifiers and Foil Trees

Zirkzee, Wouter (author)

Due to the rapid growth of diseases like diabetes, machine-learning (ML) advanced clinical decision support systems (CDSS) that support doctors or patients in their care might prove to be a valuable asset. In the battle to make healthcare more accessible, adopting ML techniques could be supportive in increasing the overall<br/>effectiveness and...

master thesis 2021

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Benchmarking Distributed Database Performance and Dependability under Partial System Failures

Bes, Ruben (author)

Many types of database management systems exist, but finding the one that is right for a specific use case is becoming increasingly more difficult. Benchmarks allow one to compare various systems, but in a world where distributed DBMSs are increasingly used for mission critical purposes, we find most existing benchmarks neglect fault tolerance...

master thesis 2021

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Determine and explain confidence in predicting violations on inland ships in the Netherlands

Bakker, Paul (author)

For real-world problems even the most complex machine learning models can only achieve a certain accuracy. This makes it important to understand why a specific prediction is made. Explanations can provide human decision support by allowing human experts to assess the reasoning of the model as well as the correctness. Specifically, in this thesis...

master thesis 2020

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Multi-model inference on the edge: Scheduling for multi-model execution on resource constrained devices

Cox, Bart (author)

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices,especially for image-based analysis, e.g., identifying objects, faces, and genders. While very successful in resource rich environments like the cloud of powerful computers, utilizing Deep Learning on edge devices for inference is not used...

master thesis 2020

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Do More Elaborate Search Strategies Lead to Better Neural Architecture Search Performance?

den Ottelander, Tom (author)

Computer vision tasks, like supervised image classification, are effectively tackled by convolutional neural networks, provided that the architecture, which defines the structure of the network, is set correctly. Neural Architecture Search (NAS) is a relatively young and increasingly popular field that is concerned with automatically optimizing...

master thesis 2020

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Interaction Design Patterns for Social Robot Assistance of Moral Decisions in Healthcare: Confrontation with Resuscitation Dilemmas

JAISHANKAR, SUJIT (author)

The implementation of social robots in the healthcare industry is becoming substantial as a consequence of the scarcity of healthcare professionals, rising costs of healthcare and an increase in the number of vulnerable populations. Social robots will be deployed, in increasing numbers, in assisting health care professionals during the provision...

master thesis 2020