Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

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 ...

Background and hypothesis: Redox dysregulation has been proposed as a convergent point of childhood trauma and the emergence of psychiatric disorders, such as schizophrenia (SCZ). A critical region particularly vulnerable to environmental insults during ad ...

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 ...

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 ...

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 ...

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 ...

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 ...

We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic ...

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 ...

Conversational tutoring systems (CTSs) offer a promising avenue for individualized learning support, especially in domains like persuasive writing. Although these systems have the potential to enhance the learning process, the specific role of learner cont ...

Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning

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

Unveiling the complexity of learning and decision-making

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

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Incorporating Projective Geometry into Deep Learning

Topics in statistical physics of high-dimensional machine learning

Social Opinion Formation and Decision Making Under Communication Trends

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

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

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

Adolescent Stress-Induced Ventral Hippocampus Redox Dysregulation Underlies Behavioral Deficits and Excitatory/Inhibitory Imbalance Related to Schizophrenia

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

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

Towards practical reinforcement learning for tokamak magnetic control

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Understanding generalization and robustness in modern deep learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Computational models of intrinsic motivation for curiosity and creativity

Glenohumeral joint force prediction with deep learning

Evaluating the Impact of Learner Control and Interactivity in Conversational Tutoring Systems for Persuasive Writing