Publications associées à Adaptive energy reference for machine-learning models of the electronic density of states

Publications associées (15)

How data structures affect generalization in Kernel Methods and Deep Learning

Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

EPFL2025

Joint Compression of Multi-model Systems for Edge Devices

Mathieu Salzmann, João Luís Prado Vieira

Deploying deep learning models for computer vision (CV) often requires a careful trade-off between performance, efficiency, and cost. In the context of edge inference of machine learning systems, this trade-off translates into a challenge, as computing and ...

Springer Science and Business Media Deutschland GmbH2025

RAFT: Regularized Adversarial Fine-Tuning to Enhance Deep Reinforcement Learning for Self-Parking

Andrea Cavallaro

Deep reinforcement learning (DRL) is a powerful method for local motion planning in automated driving. However, training of DRL agents is difficult and subject to instability. We propose Regularized Adversarial Fine-Tuning (RAFT), an adversarial DRL traini ...

2025

Adaptive Joint Distribution Learning

Damir Filipovic

We develop a new framework for estimating joint probability distributions using tensor product reproducing kernel Hilbert spaces (RKHS). Our framework accommodates a low-dimensional, normalized, and positive model of a Radon-Nikodym derivative, which we es ...

SIAM PUBLICATIONS2025

Low-Resource Speech Recognition and Understanding for Challenging Applications

Juan Pablo Zuluaga Gomez

Automatic speech recognition (ASR) and spoken language understanding (SLU) is the core component of current voice-powered AI assistants such as Siri and Alexa. It involves speech transcription with ASR and its comprehension with natural language understand ...

EPFL2024

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Zhuan Shi, Gang Wang

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system ...

IJCAI2024

Co-optimising Design and Operation of Energy Systems using Reinforcement Learning

Marine Dominique Cauz

The energy transition is ongoing, in Switzerland and abroad, leading to an ever-increasing development of renewable energy sources. These sources are decentralised, heterogeneous, come with a high dependance on weather conditions, and are therefore hard t ...

EPFL2024

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Martin Josifoski

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume ...

2023

Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells

Mohammad Khaja Nazeeruddin, François Maréchal, Naveen Bhati

Perovskite solar cells are among the most dominant emerging PV technologies which have shown unparallel improvement in power conversion efficiency in just over a decade. Long charge carrier diffusion lengths and lifetimes, high absorption coefficients, ban ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Graph Representation Learning with Optimal Transport: Analysis and Applications

Effrosyni Simou

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

EPFL2022

Automatic pathological speech assessment

Parvaneh Janbakhshi

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

EPFL2022

Atomic-Level Structure of Zinc-Modified Cementitious Calcium Silicate Hydrate

Karen Scrivener, David Lyndon Emsley, Paul Bowen, Manuel Cordova, Pinelopi Moutzouri, Amrit Venkatesh, Ziga Casar, Anna Morales Melgares

It has recently been demonstrated that the addition of zinc can enhance the mechanical strength of tricalcium silicates (C3S) upon hydration, but the structure of the main hydration product of cement, calcium silicate hydrate (C-S-H), in zinc modified form ...

AMER CHEMICAL SOC2022

A data-science approach to predict the heat capacity of nanoporous materials

Berend Smit, Mehrdad Asgari, Seyedmohamad Moosavi, Daniele Ongari, Andres Adolfo Ortega Guerrero, Elias Moubarak, Balázs Álmos Novotny, Özge Kadioglu

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most m ...

NATURE PORTFOLIO2022

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Emmanuel Abbé

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

IEEE COMPUTER SOC2022