Publications associées à Neural network software

Safe Deep Neural Networks

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

EPFL2024

Polynomial-time universality and limitations of deep learning

Emmanuel Abbé

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

WILEY2023

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Friederike Metz

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...

NATURE PORTFOLIO2023

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Michaël Unser

The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying regularization functional that depends on an operator L\documentclass[12pt]{ ...

Springer2023

Backpropagation-free training of deep physical neural networks

Romain Christophe Rémy Fleury, Ali Momeni, Babak Rahmani, Matthieu Francis Malléjac, Marc Philipp Del Hougne

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

American Association for the Advancement of Science2023

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Jürg Alexander Schiffmann, Soheyl Massoudi, Cyril Picard

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

ASME Amer Soc Mechanical Engineers2023

Neural tensor contractions and the expressive power of deep neural quantum states

Giuseppe Carleo

We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction of neural-network layers that efficiently perform tensor contractions and that use commonly adopt ...

AMER PHYSICAL SOC2022

Advances In Morphological Neural Networks: Training, Pruning And Enforcing Shape Constraints

Nikolaos Dimitriadis

In this paper, we study an emerging class of neural networks, the Morphological Neural networks, from some modern perspectives. Our approach utilizes ideas from tropical geometry and mathematical morphology. First, we state the training of a binary morphol ...

IEEE2021

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Nirupam Gupta, Shuo Liu

This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a ce ...

IEEE COMPUTER SOC2021

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Michaël Unser, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

2020

Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing ...

EPFL2020

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network

Hyunjin Shim

Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-poi ...

2019

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Jan Sickmann Hesthaven, Stefano Ubbiali

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

2018

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

Qian Wang, Jan Sickmann Hesthaven, Deep Ray

A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the redu ...

2018

Learning to Assign Orientations to Feature Points

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Yannick Verdie

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction ...

2016