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Scale Learning in Scale-Equivariant Convolutional networks

Basting, Mark (author)

In real-life scenarios, there are many variations in sizes of objects of the same category and the objects are not always placed at a fixed distance from the camera. This results in objects taking up an arbitrary size of pixels in the image. Vanilla CNNs are by design only translation equivariant and thus have to learn separate filters for...

master thesis 2023

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Scripted AI for Overcooked: Designing and Evaluating a Scripted AI Controller for Simplified Overcooked

Anton, Mihai (author)

Overcooked, an immersive multiplayer video game centered around cooperative cooking challenges, provides the roots for this research project. The study focuses on designing and evaluating a hand-authored controller in comparison to controllers implemented using various machine learning techniques, such as Population Based Training, in the...

bachelor thesis 2023

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Getting AI to Cooperate: Sharing a Critic in a Video Game

Groenendijk, Jelle (author)

The popular video game "Overcooked" is a great example of a task requiring complex planning and cooperation with other players. This game is used as the inspiration for an environment for evaluating AI, called "Overcooked-AI". This paper implements a centralized critic into the Overcooked-AI environment's implementation of the PPO algorithm and...

bachelor thesis 2023

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Cooperative AI for Overcooked: Multi-Agent RL with Population-Based Training

Nestorov, Ivan (author)

In ad-hoc cooperative environments, the usage of artificial intelligence to take supportive roles and work in collaboration with humans has proven to be of great benefit. The objective of this research is to evaluate the use of population-based training for reinforcement learning agents in a simplified version of the multiplayer game -...

bachelor thesis 2023

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The Effects of Heuristic Optimisations on Planning Algorithms Within Cooperative AI: Cooperative Planning in Overcooked

Herben, Jonte (author)

Cooperative AI is AI designed to cooperate with humans. One example of such an AI, made using planning algorithms, was studied in a paper from 2019 which used a simplified version of the video game Overcooked for evaluation. However, only limited evaluations were possible due to the long runtime and heuristic optimisations made. This paper will...

bachelor thesis 2023

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Improvements in Imitation Learning for Overcooked

Niemantsverdriet, Duuk (author)

Arguably the main goal of artificial intelligence is to create agents that can collaborate with humans to achieve a shared goal. It has been shown that agents that assume their partner to be optimal can converge to protocols that humans do not understand. Taking human suboptimality into consideration is imperative to perform well in a...

bachelor thesis 2023

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Accelerating t-SNE for hyperbolic embeddings

van Geffen, Hunter (author)

t-distributed stochastic neighbor embedding (t-SNE) is a dimensionality reduction technique able to embed high-dimensional data into 2 or 3 dimensions for the purpose of visualization, and has proven to be useful in various applications, such as single-cell analysis. Normally t-SNE embeds into Euclidean space, but recent work shows that...

master thesis 2022

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Discovering the effect of hero choice on the outcome of a Dota 2 game

Toksoy, Noyan (author)

Dota 2 is one of the most popular MOBA (Multiplayer Online Battle Arena) games being played today. A Dota 2 match is played by two teams of 5 players. The main goal of the game is to destroy the opposing team’s Ancient tower, the team that manages to do so, wins the game. An essential part of a match is the hero selection phase before it starts....

bachelor thesis 2022

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Causal Inference in DotA 2 when estimated through randomized data

Avgousti, Stelios (author)

Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the built-in randomization help with testing causal inference in a scenario where in reality it would be hard and expensive. Randomized data in coherence with causal inference is well documented and tested, but not...

bachelor thesis 2022

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Finding the causal effect of picking Slark in the game of Dota 2 on the final match outcome based on in-match events using the front-door adjustment

Liang, Hendy (author)

The front-door adjustment is a causal inference method with which it is possible to determine the causal effect of applying a treatment given a setting which satisfies the front-door criterion. This involves having a mediator through which all the causal effect flows from treatment to outcome. The front-door adjustment adjusts for confounders...

bachelor thesis 2022

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Estimating the effect of ‘diverse’ team compositions on Dota 2 game outcomes using Inverse Probability Weighting

Goedhart, Christof (author)

Commonly, when researchers are figuring out the effect of a putative cause, additional variables influence the cause and the effect. These are called confounders, and they obfuscate causal relationships. Inverse Probability Weighting is a method that can be applied to remove confounding and show a causal effect. This study aims to determine if...

bachelor thesis 2022

document

Multi-modal deep learning based 3D image registration for osseous anatomies

van Ulsen, Hidde (author)

Image registration is a fundamental requirement for many medical applications. In recent years, deep learning approaches for registration have shown to be a promising alternative to conventional methods. However, most learning based methods do not consider the different physical properties of various tissues, which can result in unrealistic...

master thesis 2022

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Kallisto Repurposed: Using sequencing reads from the spike, nucleocapsid, and a middle region of nsp3 in the kallisto pipeline to better predict SARS-CoV-2 variants in wastewater

Anton, Matei (author)

During a viral infection, we expel remnants of the virus. This makes it possible to conduct wastewater analysis which aid in the efforts to track the evolution of the current Covid-19 pandemic. It has been shown that by repurposing the kallisto algorithm, the abundance of SARS-CoV-2 variants in wastewater samples can be estimated. Since this is...

bachelor thesis 2022

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The impact of sequencing errors and contaminating viruses on SARS-CoV-2 variant detection by sequencing wastewater-sourced viral RNA

van der Lugt, Mart (author)

Since the start of the SARS-CoV-2 pandemic, the monitoring of SARS-CoV-2 by way of viral RNA sequencing of wastewater has proven to be an efficient and effective way of estimating COVID-19 cases in population groups. A recently developed pipeline also enables us to estimate SARS-CoV-2 variant abundance using viral samples from wastewater. This...

bachelor thesis 2022

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Effect of Output Granularity on SARS-CoV-2 Variant Abundance Estimates using Domestic Wastewater Sequencing

Kalia, Yash (author)

Monitoring of SARS-CoV-2 variants is crucial to efforts in combating the COVID-19 pandemic. Lineage level abundance estimates for SARS-CoV-2 can be obtained from viral material present in domestic wastewater. The abundance predictions can be made at different levels of granularity-individual lineage level(high granularity) or variant level(low...

bachelor thesis 2022

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SARS-CoV-2 lineage abundance quantification in wastewater: a benchmark study for the identification of optimal reference set design

Nika, Ioanna (author)

Lineage abundance estimation of SARS-CoV-2 in wastewater is a technique that aims to monitor the lineage prevalence in communities and help contain the COVID-19 pandemic. Lineages are collections of closely related mutants of a virus. It is suggested that the genome sequences of lineages differ across the globe due to random mutations or...

bachelor thesis 2022

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Latent Shape Editing

Baas, Berend (author)

Existing work in shape editing applications using deep learning has primarily focused on shape interpolation.<br/>We propose a pair of techniques that utilize the latent space of deformation networks to provide control schemes for semantic shape editing applications.<br/><br/>The first technique presented utilizes linear directions as...

master thesis 2021

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Are CNNs that Learn to Predict Image Statistics Invariant to Domain Shifts?

Biesheuvel, Julian (author)

Yes, convolutional neural networks are domain-invariant, albeit to some limited extent. We explored the performance impact of domain shift for convolutional neural networks. We did this by designing new synthetic tasks, for which the network’s task was to map images to their mean, median, standard deviation, and variance pixel intensities. We...

bachelor thesis 2021

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How does imbalanced data affect performance of regression CNNs?

Thakoersingh, Ratish (author)

This research provides an overview on how training Convolutional Neural Networks (CNNs) on imbalanced datasets affect the performance of the CNNs. Datasets could be imbalanced as a result of several reasons. There are for example naturally less samples of rare diseases. Since the network is trained less on those instances, this might lead to...

bachelor thesis 2021

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Is the batch size affecting the performance of Regression CNNs ?

Lamon, Julien (author)

With an expectation of 8.3 trillion photos stored in 2021 [1], convolutional neural networks (CNN) are beginning to be preeminent in the field of image recognition. However, with this deep neural network (DNN) still being seen as a black box, it is hard to fully employ its capabilities. A need to tune hyperparameters is required to have a robust...

bachelor thesis 2021