Publications associées à ∇<sup>2</sup>DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

Publications associées (20)

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

2025

Differences in Visual Perception in Humans and Deep Neural Networks

Ben Henrik Lönnqvist

Visual grouping is the ability to combine parts of objects to form whole objects, and it is crucial for general and robust vision. Despite this, a comprehensive model of how the process works in humans remains elusive. In my thesis, I investigate how well ...

EPFL2025

Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord

Alessandro Pazzaglia, Auke Ijspeert, Jonathan Patrick Arreguit O'Neill, Andrea Ferrario

This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplifi ...

2025

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

Sohyeong Kim

As urban populations continue to expand and vehicle density increases, the need for effective traffic monitoring and management becomes increasingly critical. Urban areas face significant challenges such as growing congestion, safety concerns, and parking ...

EPFL2025

Development of Small Molecule Heparin Glycomimetics for Applications in Nerve Regeneration Therapies

Melis Özkan

Heparin and heparan sulfate (HS) glycosaminoglycans (GAGs), essential components of the extracellular matrix (ECM), regulate a vast array of biological processes by modulating protein interactions and cellular signaling. Their structural complexity, driven ...

EPFL2025

Hierarchical Training of Deep Neural Networks Using Early Exiting

Pascal Frossard, Liza Carol Andrea Dunbar, Ahmet Caner Yüzügüler, Yamin Sepehri

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Programming nonlinear propagation for efficient optical learning machines

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ugur Tegin, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power com ...

Spie-Soc Photo-Optical Instrumentation Engineers2024

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce, Luca Arnaboldi

We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

JMLR2024

A Joint Graph Signal and Laplacian Denoising Network

Zepeng Zhang

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause sign ...

Institute of Electrical and Electronics Engineers Inc.2024

NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila

Victor Alfred Stimpfling, Louise Genoud, Sibo Wang, Pavan P Ramdya, Thomas Ka Chung Lam, Femke Imreh Hurtak, Pembe Gizem Ozdil

Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Such models have primarily been used to investigate motor control with less emphasis on how the brain and motor syste ...

Nature Publishing Group2024

Closing the Gap to Quadratic Invariance: A Regret Minimization Approach to Optimal Distributed Control

Giancarlo Ferrari Trecate, Luca Furieri, Daniele Martinelli

In this work, we focus on the design of optimal controllers that must comply with an information structure. State-of-the-art approaches do so based on the H2 or H∞ norm to minimize the expected or worst-case cost in the presence of stochastic or adversaria ...

IEEE2024

Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Frédéric Courbin, Georges Meylan, Austin Chandler Peel

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samp ...

2024

Forward-forward training of an optical neural network

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics ...

Optica Publishing Group2023

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Jessy Lauer, Mu Zhou

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

2023

Stochastic Approximation of Variational Quantum Imaginary Time Evolution

Giuseppe Carleo

The imaginary-time evolution of quantum states is integral to various fields, ranging from natural sciences to classical optimization or machine learning. Since simulating quantum imaginary-time evolution generally requires storing an exponentially large w ...

Institute of Electrical and Electronics Engineers Inc.2023

Scale-out Systolic Arrays

Pascal Frossard, Babak Falsafi, Ahmet Caner Yüzügüler, Canberk Sönmez

Multi-pod systolic arrays are emerging as the architecture of choice in DNN inference accelerators. Despite their potential, designing multi-pod systolic arrays to maximize effective throughput/Watt-i.e., throughput/Watt adjusted when accounting for array ...

ASSOC COMPUTING MACHINERY2023

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

Martin Jaggi, Luis Felipe Barba Flores

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

SPRINGERNATURE2022

Chronic recording of cortical activit y underlying vocalization in awake minipigs

Stéphanie Lacour, Giuseppe Schiavone, Florian Dylan Fallegger

Background: Investigating brain dynamics underlying vocal production in animals is a powerf u l w a y to inform on the neural bases of human speech. In particular, brain networks underlying vocal production in non-human primates show striking similarities ...

ELSEVIER2022

NEST: The Neural Simulation Tool

Marc-Oliver Gewaltig

Springer Science+Business Media2022

Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials

Federico Grasselli

The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely, the fluorite-structure materials PbF2, CaF2, and UO2 (type II), and the a ...

AIP Publishing2022