Imagen de Portada

A theoretical perspective on mode collapse in variational inference

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Soletskyi, Roman, Gabrié, Marylou, Loureiro, Bruno
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Disordered Systems and Neural Networks Statistical Mechanics Statistics Theory
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

The critical slowing down in diffusion models
por: Del Bono, Luca Maria, et al.
Publicado: (2026)

A theoretical framework for overfitting in energy-based modeling
por: Catania, Giovanni, et al.
Publicado: (2025)

The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data
por: Takahashi, Takashi
Publicado: (2022)

PCA recovery thresholds in low-rank matrix inference with sparse noise
por: Adomaityte, Urte, et al.
Publicado: (2025)

Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems
por: Angelini, Maria Chiara, et al.
Publicado: (2023)

Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
por: Ghio, Davide, et al.
Publicado: (2023)

Gaussian mixtures and non-parametric likelihoods through the lens of statistical mechanics
por: Ghosh, Subhroshekhar, et al.
Publicado: (2026)

Two failure modes of deep transformers and how to avoid them: a unified theory of signal propagation at initialisation
por: Giorlandino, Alessio, et al.
Publicado: (2025)

On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds
por: Vilucchio, Matteo, et al.
Publicado: (2024)

Asymptotics of Learning with Deep Structured (Random) Features
por: Schröder, Dominik, et al.
Publicado: (2024)

High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
por: Adomaityte, Urte, et al.
Publicado: (2023)

A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights
por: Ricci, Fabiola, et al.
Publicado: (2026)

Some observations on the ambivalent role of symmetries in Bayesian inference problems
por: Semerjian, Guilhem
Publicado: (2025)

A replica analysis of under-bagging
por: Takahashi, Takashi
Publicado: (2024)

Machine learning the Ising transition: A comparison between discriminative and generative approaches
por: Zhang, Difei, et al.
Publicado: (2024)

Statistical mechanics of extensive-width Bayesian neural networks near interpolation
por: Barbier, Jean, et al.
Publicado: (2025)

Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks
por: Nguyen, Minh-Toan, et al.
Publicado: (2026)

Graphical model for factorization and completion of relatively high rank tensors by sparse sampling
por: Cavaliere, Angelo Giorgio, et al.
Publicado: (2025)

Overparametrization bends the landscape: BBP transitions at initialization in simple Neural Networks
por: Annesi, Brandon Livio, et al.
Publicado: (2025)

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation
por: Barbier, Jean, et al.
Publicado: (2025)

Optimal generalisation and learning transition in extensive-width shallow neural networks near interpolation
por: Barbier, Jean, et al.
Publicado: (2025)

Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs
por: Braunstein, Alfredo, et al.
Publicado: (2025)

Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
por: Fachechi, Alberto, et al.
Publicado: (2024)

Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
por: Mori, Francesco, et al.
Publicado: (2024)

Discrete generative diffusion models without stochastic differential equations: a tensor network approach
por: Causer, Luke, et al.
Publicado: (2024)

Transfer Learning in $\ell_1$ Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis
por: Okajima, Koki, et al.
Publicado: (2024)

Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis
por: Lee, Donghee, et al.
Publicado: (2024)

Generalization vs. Specialization under Concept Shift
por: Nguyen, Alex, et al.
Publicado: (2024)

Gaussian Universality in Neural Network Dynamics with Generalized Structured Input Distributions
por: Bae, Jaeyong, et al.
Publicado: (2024)

Coding schemes in neural networks learning classification tasks
por: van Meegen, Alexander, et al.
Publicado: (2024)

Nonequilbrium physics of generative diffusion models
por: Yu, Zhendong, et al.
Publicado: (2024)

Spring-block theory of feature learning in deep neural networks
por: Shi, Cheng, et al.
Publicado: (2024)

Fast training and sampling of Restricted Boltzmann Machines
por: Béreux, Nicolas, et al.
Publicado: (2024)

Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers
por: Tiberi, Lorenzo, et al.
Publicado: (2024)

The Role of the Time-Dependent Hessian in High-Dimensional Optimization
por: Bonnaire, Tony, et al.
Publicado: (2024)

Message Passing Variational Autoregressive Network for Solving Intractable Ising Models
por: Ma, Qunlong, et al.
Publicado: (2024)

Cascade of phase transitions in the training of Energy-based models
por: Bachtis, Dimitrios, et al.
Publicado: (2024)

Analytic theory of dropout regularization
por: Mori, Francesco, et al.
Publicado: (2025)

The Copycat Perceptron: Smashing Barriers Through Collective Learning
por: Catania, Giovanni, et al.
Publicado: (2023)

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
por: Biazzo, Indaco
Publicado: (2023)