Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning

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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 ...

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

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 ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

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 the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint ...

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 ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

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 ...

Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning

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Topics in statistical physics of high-dimensional machine learning

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Network time series forecasting in photovoltaics power production

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Social Opinion Formation and Decision Making Under Communication Trends

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Predicting protein interactions using geometric deep learning on protein surfaces

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Glenohumeral joint force prediction with deep learning

Meta-learning to address diverse Earth observation problems across resolutions

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

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

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)