Improving Multi-agent Coordination by Learning to Estimate Contention

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Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinem ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...

Example Data for DeepReefMap This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with De ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

Improving Multi-agent Coordination by Learning to Estimate Contention

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Towards practical reinforcement learning for tokamak magnetic control

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Unveiling the complexity of learning and decision-making

Social Opinion Formation and Decision Making Under Communication Trends

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Incorporating Projective Geometry into Deep Learning

Predicting the long-term collective behaviour of fish pairs with deep learning

Partial discharge localization in power transformer tanks using machine learning methods

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Topics in statistical physics of high-dimensional machine learning

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Stochastic pairwise preference convergence in Bayesian agents

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Understanding generalization and robustness in modern deep learning

Glenohumeral joint force prediction with deep learning