Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

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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 ...

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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 ...

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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Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

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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

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

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

Understanding generalization and robustness in modern deep learning

Social Opinion Formation and Decision Making Under Communication Trends

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Incorporating Projective Geometry into Deep Learning

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

Topics in statistical physics of high-dimensional machine learning

Unveiling the complexity of learning and decision-making

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

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

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

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Towards practical reinforcement learning for tokamak magnetic control

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Glenohumeral joint force prediction with deep learning

Meta-learning to address diverse Earth observation problems across resolutions

Network time series forecasting in photovoltaics power production