Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

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Measuring animal behaviors is a crucial task in a range of scientific applications. Modern approaches with deep neural networks calls for developing solutions that solve a wide range of vision and language tasks. This ubiquitous need for understanding beha ...

Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn data distributions and generate synthetic data. Their importance in transportation research is increasingly recogn ...

Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FO ...

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 ...

Microscopy is one of the essential tools to quantify and advance our understanding of living systems. Modern digital microscopes enable measurements at unprecedented spatiotemporal scale, so automated processing methods are required to complete tasks such ...

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, ...

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 ...

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 ...

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 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 ...

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 ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

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 ...

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 ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

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 ...

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 ...

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 ...

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a ...

In recent years, exploring deep generative models for generating time series has garnered significant interest within the research community. These models have found wide-ranging applications in areas such as data augmentation, scenario simulation, and the ...

Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

Chattez avec Graph Search

Neuroscience-inspired computer vision and language modeling for behavior analysis

A gentle introduction and tutorial on Deep Generative Models in transportation research

Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains

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

Label-Efficient Deep Learning Methods for Event Detection, Object Segmentation and Cell Tracking in Biological Microscopy Images

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

Unveiling the complexity of learning and decision-making

Topics in statistical physics of high-dimensional machine learning

Incorporating Projective Geometry into Deep Learning

Understanding generalization and robustness in modern deep learning

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

Driving and suppressing the human language network using large language models

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Social Opinion Formation and Decision Making Under Communication Trends

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

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

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

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

White adipose tissue distribution and amount are associated with increased white matter connectivity

One-Pass Generation of Multivariate Time Series through Conditional Multivariate Modeling