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Hidden-nucleons neural-network quantum states for the nuclear many-body problem
Related publications (33)
Gibbs sampling the posterior of neural networks
Lenka Zdeborová, Giovanni Piccioli, Emanuele Troiani
In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...
Bristol2024An exact mapping from ReLU networks to spiking neural networks
Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Giovanni Cherubini, Angeliki Pantazi
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...
2023Predicting in Uncertain Environments: Methods for Robust Machine Learning
One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...
EPFL2022Sharp asymptotics on the compression of two-layer neural networks
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimiz ...
IEEE2022Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
EPFL2022The spectral bias of polynomial neural networks
Volkan Cevher, Grigorios Chrysos, Leello Tadesse Dadi, Moulik Choraria
Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...
2022Improving the Training of Compact Neural Networks for Visual Recognition
During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...
EPFL2022Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu, Yongtao Wu
Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural network architectures, but it is incomplete for neural networks with Hadamar ...
2022Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems
In this thesis, we reveal that supervised learning and inverse problems share similar mathematical foundations. Consequently, we are able to present a unified variational view of these tasks that we formulate as optimization problems posed over infinite-di ...
EPFL2022Nuclei with Up to A=6 Nucleons with Artificial Neural Network Wave Functions
The ground-breaking works of Weinberg have opened the way to calculations of atomic nuclei that are based on systematically improvable Hamiltonians. Solving the associated many-body Schrodinger equation involves non-trivial difficulties, due to the non-per ...
SPRINGER WIEN2022