Publications related to Universality laws for Gaussian mixtures in generalized linear models

Related publications (17)

Reverse engineering primate sensorimotor control with machine learning

The ability of the brain to produce adaptive and dexterous movements relies on the seamless integration of sensory feedback into motor commands. Despite significant advances in motor neuroscience, critical gaps persist in understanding how proprioceptive a ...

EPFL2025

Uncertainty quantification in groundwater volume predictions from seismic data using neural networks

Jan Sickmann Hesthaven

Optimum groundwater management is important for addressing the global challenges of water scarcity, especially in arid regions. This study demonstrates the application of synthetic data combined with seismic methods to quantify water volumes in a controlle ...

European Association of Geoscientists and Engineers, EAGE2024

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Wenlong Liao

Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...

2024

A 2.46-mm2 Miniaturized Brain-Machine Interface (MiBMI) Enabling 31-Class Brain-to-Text Decoding

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri, Amitabh Yadav

Recent advancements in brain-machine interface (BMI) technology offer groundbreaking solutions for individuals with motor impairments, potentially extending to speech synthesis and handwriting assistance. However, current BMIs rely on cumbersome benchtop s ...

2024

Learning Time-Scales in Two-Layers Neural Networks

Raphaël Jean Berthier

Gradient-based learning in multi-layer neural networks displays a number of striking features. In particular, the decrease rate of empirical risk is non-monotone even after averaging over large batches. Long plateaus in which one observes barely any progre ...

2024

Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

Corinne Louise Jones

Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods different in essence from kernel-based methods. In this work we translate several classical convolutional neural networks into kernel-based counterparts. Ea ...

2023

The statistical complexity of early-stopped mirror descent

Tomas Vaskevicius

Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...

Oxford University Press2023

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Tanja Christina Käser Jacober, Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Bayes-optimal inference for spreading processes on random networks

Lenka Zdeborová, Davide Ghio, Indaco Biazzo

We consider a class of spreading processes on networks, which generalize commonly used epidemic models such as the SIR model or the SIS model with a bounded number of reinfections. We analyze the related problem of inference of the dynamics based on its pa ...

Amer Physical Soc2023

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

Juan Pablo Madrigal Cianci

This thesis is devoted to the construction, analysis, and implementation of two types of hierarchical Markov Chain Monte Carlo (MCMC) methods for the solution of large-scale Bayesian Inverse Problems (BIP). The first hierarchical method we present is base ...

EPFL2022

Event-driven acquisition for content-enriched microscopy

Suliana Manley, Dora Mahecic, Martin Weigert, Juliette Griffie, Chen Zhang, Willi Leopold Stepp

Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution. A common goal ...

Nature Publishing Group2022

Teaching machines how to find strongly lensed galaxies in cosmological sky surveys

Elodie Marie Charlène Savary

Analyzing gravitationally lensed objects enables a wide range of physical and cosmological applications, such as probing the dark matter content in galaxies and clusters or measuring the Universe's expansion rate. The precision of these applications can be ...

EPFL2022

Complex Analysis meets Statistical Mechanics: Applications to Kernel Methods and Conformal Field Theory

Francesco Spadaro

This thesis is centered on questions coming from Machine Learning (ML) and Statistical Field Theory (SFT). In Machine Learning, we consider the subfield of Supervised Learning (SL), and in particular regression tasks where one tries to find a regressor tha ...

EPFL2021

Adaptive Path Interpolation Method for Sparse Systems: Application to a Censored Block Model

Nicolas Macris, Jean François Emmanuel Barbier, Chun Lam Chan

Recently, a new adaptive path interpolation method has been developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gen ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Gabriele Sicuro

Generalised linear models for multi-class classification problems are one of the fundamental building blocks of modern machine learning tasks. In this manuscript, we characterise the learning of a mixture of K Gaussians with generic means and covariances v ...

2021

Exploiting Errors for Efficiency: A Survey from Circuits to Applications

Babak Falsafi, Alexandros Daglis, Dorde Jevdic, Eva Darulova, Mattia Cacciotti

When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, system software, and programming language compilers or their runtime systems can trade deviations from cor ...

ASSOC COMPUTING MACHINERY2020

Exact Preimages of Neural Network Aircraft Collision Avoidance Systems

François Fleuret, Kyle Michael Matoba

A common pattern of progress in engineering has seen deep neural networks displacing human-designed logic. There are many advantages to this approach, divorcing decisionmaking from human oversight and intuition has costs as well. One is that deep neural ne ...

2020