Publications related to The physics of energy-based models

Related publications (17)

Computational Imaging: a Multi-Facet Perspective

Computational Imaging (CI) is the field concerned with reconstructing images of some quantity of interest from a finite number of measurements. It is pervasive in science and engineering problems by enabling the reconstruction of images from a variety of m ...

EPFL2025

A new age in protein design empowered by deep learning

Bruno Emanuel Ferreira De Sousa Correia, Michael Bronstein, Casper Alexander Goverde, Arne Schneuing, Hamed Khakzad, Ilia Igashov

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Cell Press2023

Critical parametric quantum sensing

Fabrizio Minganti

Critical quantum systems are a promising resource for quantum metrology applications, due to the diverging susceptibility developed in proximity of phase transitions. Here, we assess the metrological power of parametric Kerr resonators undergoing driven-di ...

2023

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Pierre Dillenbourg, Tanja Christina Käser Jacober, Richard Lee Davis, Thiemo Wambsganss, Kevin Gonyop Kim, Wei Jiang

This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creati ...

Association for Computing Machinery2023

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022

Understanding Deep Neural Networks using Adversarial Attacks

Krishna Kanth Nakka

Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even though the discriminative power of DNNs is nowadays unquestionable, serious concerns have ...

EPFL2022

Multi-layer State Evolution Under Random Convolutional Design

Lenka Zdeborová, Florent Gérard Krzakala

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

2022

A Structured Dictionary Perspective on Implicit Neural Representations

Gizem Yüce, Pascal Frossard, Guillermo Ortiz Jimenez, Beril Besbinar

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

IEEE COMPUTER SOC2022

Using Animal Motion Capture to Learn Neural Representations

Semih Günel

Understanding behavior from neural activity is a fundamental goal in neuroscience. It has practical applications in building robust brain-machine interfaces, human-computer interaction, and assisting patients with neurological disabilities. Despite the eve ...

EPFL2022

The Food Recognition Benchmark: Using Deep Learning to Recognize Food in Images

Marcel Salathé, Harris Héritier, Djilani James Kebaili, Victor Guy Boulanger

The automatic recognition of food on images has numerous interesting applications, including nutritional tracking in medical cohorts. The problem has received significant research attention, but an ongoing public benchmark on non-biased (i.e., not scraped ...

FRONTIERS MEDIA SA2022

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

2022

Measurement of the W boson mass

Olivier Schneider, Aurelio Bay, Guido Haefeli, Tatsuya Nakada, Frédéric Blanc, Lesya Shchutska, Elena Graverini, Michel De Cian, Vladimir Macko, Federico Leo Redi, Sebastian Schulte, Donal Patrick Hill, Tara Nanut, Minh Tâm Tran, Guillaume Max Pietrzyk, Maria Vieites Diaz, Marie Theres Christin Bachmayer, Lino Ferreira Lopes, Matthieu Philippe Luther Marinangeli, Serhii Cholak, Veronica Sølund Kirsebom, Ettore Zaffaroni, Surapat Ek-In, Ana Bárbara Rodrigues Cavalcante, Sara Celani, Carina Trippl, Sonia Amina Bouchiba, Thi Dung Nguyen, Alison Maria Tully, Mâu Chung Nguyên, Maarten Willibrord Uriël Van Dijk

The W boson mass is measured using proton-proton collision data at root s = 13 TeV corresponding to an integrated luminosity of 1.7fb(-1) recorded during 2016 by the LHCb experiment. With a simultaneous fit of the muon q/p(T) distribution of a sample of W ...

Springer Nature2022

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Nicolas Henri Bernard Flammarion

We consider the problem of sampling from a density of the form p(x) ? exp(-f (x) - g(x)), where f : Rd-+ R is a smooth function and g : R-d-+ R is a convex and Lipschitz function. We propose a new algorithm based on the Metropolis-Hastings framework. Under ...

Microtome Publishing2022

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Federico Grasselli, Paolo Pegolo

Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic co ...

NATURE PORTFOLIO2022

New insights on divertor parallel flows,E x B drifts, and fluctuations from in situ, two-dimensional probe measurement in the Tokamak a Configuration Variable

Basil Duval, Holger Reimerdes, Christian Gabriel Theiler, Cedric Kar-Wai Tsui, Artur Perek, Hugo De Oliveira, Sophie Danielle Angelica Gorno

In situ, two-dimensional (2D) Langmuir probe measurements across a large part of the TCV outer divertor are reported in L-mode discharges with and without divertor baffles. This provides detailed insights into time averaged profiles, particle fluxes, and f ...

IOP Publishing Ltd2022

Quantum coherent microwave-optical transduction using high-overtone bulk acoustic resonances

Tobias Kippenberg, Terence Albert Emile Blésin

A device capable of converting single quanta of the microwave field to the optical domain is an outstanding endeavor in the context of quantum interconnects between distant superconducting qubits, but likewise can have applications in other fields, such as ...

AMER PHYSICAL SOC2021

node2coords: Graph Representation Learning with Wasserstein Barycenters

Pascal Frossard, Ntorina Thanou, Effrosyni Simou

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021