Reinforcement learning: the effect of environment

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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 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 ...

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 ...

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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 ...

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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 ...

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, ...

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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 learning: the effect of environment

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Social Opinion Formation and Decision Making Under Communication Trends

Incorporating Projective Geometry into Deep Learning

Unveiling the complexity of learning and decision-making

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

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Robust machine learning for neuroscientific inference

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

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

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

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Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

Aggregating Spatial and Photometric Context for Photometric Stereo

Multimodal transcriptomic atlases of mouse spinal cord injury

Meta-learning to address diverse Earth observation problems across resolutions

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

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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