Publications associées à Teaching oxidation states to neural networks

Publications associées (20)

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Luciano Loris Viteritti, Giuseppe Carleo

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

2025

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Pierre Vandergheynst

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation lear ...

Nature Publishing Group2025

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Eklavya Sarkar

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

EPFL2025

Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence

Matin Ansaripour

Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes may not ...

Association for the Advancement of Artificial Intelligence2025

Musculoskeletal motor control with reinforcement learning

Alberto Silvio Chiappa

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. This thesis presents a collection of ...

EPFL2025

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Xin Chen

Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequ ...

Wiley2025

Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties

Meret Aeppli, Lorenz Schwab

This dataset supports the findings reported in the manuscript titled: Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties, submitted to Nature Communications. Understanding the kinetics of manganese oxi ...

Zenodo2025

Selective light olefin synthesis with high ethylene abundance via CO2 hydrogenation over (Ga-In)2O3/SSZ-13 catalysts

Andreas Züttel, Mounir Driss Mensi, Laura Piveteau, Yasemen Kuddusi

Direct light olefin synthesis from CO2 hydrogenation is a new pathway to decarbonize the chemical industry. Inspired by the promising catalytic activity of Ga2O3-based catalysts in alkane dehydrogenation, this study reveals that optimizing Ga content in th ...

2025

Insights into the Overcharge-Induced Failure Mechanism of Lithium–Sulfur Batteries

Frank Nüesch, Mohammad Jafarpour, Shungui Deng

In the pursuit of durable lithium–sulfur (Li–S) batteries, considerable research has been devoted to extending cycle life by developing advanced cathode materials. However, there exists a common overcharging problem which leads to the disfunction of Li–S b ...

2025

Force-Matching-Based Approach for the Generation of Polarizable and Nonpolarizable Force Fields Applied to CsPbI3

Cecilia Vona, Ursula Röthlisberger, Martin Peter Bircher, Mathias Dankl, Ariadni Boziki

Lead halide perovskites have emerged as highly efficient solar cell materials. However, to date, the most promising members of this class are polymorphs in which a wide-band-gap delta phase competes with the photoactive perovskite alpha form and the intrin ...

AMER CHEMICAL SOC2025

Learning the language of the genome: AI- and data-driven decoding of genetic variants and disease mechanisms

Ali Saadat Varnosfaderanii

Genetic variation plays a crucial role in human disease susceptibility and molecular pathophysiology. This thesis explores genetic determinants of disease using advanced computational approaches, including statistical modeling, network analysis, and deep l ...

EPFL2025

Operando evidence on the chirality-enhanced oxygen evolution reaction in intrinsically chiral electrocatalysts

Magalí Alejandra Lingenfelder, Yunchang Liang

Electrolytic hydrogen is identified as a crucial component in the desired decarbonisation of the chemical industry, utilizing renewable energy to split water into hydrogen and oxygen. Water electrolysis still requires important scientific advances to impro ...

ROYAL SOC CHEMISTRY2025

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

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 ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements gene ...

Zenodo2024

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Ortal Yona Senouf, Ivan-Daniel Sievering

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

2024

Recursively Feasible Chance-Constrained Model Predictive Control Under Gaussian Mixture Model Uncertainty

Maryam Kamgarpour, Kai Ren

We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit multiple ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Guillaume Romain Obozinski

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale ...

San Francisco2024

The influence of data on adversarial robustness

Javier Alejandro Maroto Morales

Deep learning models are generally vulnerable to small perturbations, raising concerns about safety, security, and performance guarantees. Achieving robustness has been shown to be costly in terms of computational resources and quantity of data. This thesi ...

EPFL2024

Rationally Designing the Supramolecular Interfaces of Nanoparticle Superlattices with Multivalent Polymers

Daryl Yee Wei Liang

In supramolecular materials, multiple weak binding groups can act as a single collective unit when confined to a localized volume, thereby producing strong but dynamic bonds between material building blocks. This principle of multivalency provides a versat ...

Amer Chemical Soc2024

Training a Filter-Based Model of the Cochlea in the Context of Pre-Trained Acoustic Models

Philip Neil Garner, Louise Clothilde Coppieters De Gibson

Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-boxes. ...

2024