Imagen de Portada

Symmetries in Overparametrized Neural Networks: A Mean-Field View

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Maass, Javier, Fontbona, Joaquin
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Probability
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD
por: Wan, Yijun, et al.
Publicado: (2023)

ResNets of All Shapes and Sizes: Convergence of Training Dynamics in the Large-scale Limit
por: Chaintron, Louis-Pierre, et al.
Publicado: (2026)

An Explicit Euler-type Scheme for Lévy-driven SDEs with Superlinear and Time-Irregular Coefficients
por: Biswas, Sani, et al.
Publicado: (2025)

Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
por: Fouque, Jean-Pierre, et al.
Publicado: (2025)

A Mean-Field Theory of $Θ$-Expectations
por: Qi, Qian
Publicado: (2025)

Dropout Neural Network Training Viewed from a Percolation Perspective
por: Devlin, Finley, et al.
Publicado: (2025)

A Mean Field Ansatz for Zero-Shot Weight Transfer
por: Chen, Xingyuan, et al.
Publicado: (2024)

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
por: Tankala, Chandan, et al.
Publicado: (2025)

The Mean-Field Dynamics of Transformers
por: Rigollet, Philippe
Publicado: (2025)

Partially Stochastic Infinitely Deep Bayesian Neural Networks
por: Calvo-Ordonez, Sergio, et al.
Publicado: (2024)

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
por: Altekrüger, Fabian, et al.
Publicado: (2023)

On the Epistemic Uncertainty of Overparametrized Neural Networks
por: Rügamer, David
Publicado: (2026)

Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization
por: Jakub, Cameron, et al.
Publicado: (2023)

Random ReLU Neural Networks as Non-Gaussian Processes
por: Parhi, Rahul, et al.
Publicado: (2024)

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit
por: Riedl, Konstantin, et al.
Publicado: (2026)

Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
por: Holberg, Christian, et al.
Publicado: (2024)

Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
por: Di Persio, Luca, et al.
Publicado: (2026)

Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle
por: Giovagnini, Filippo, et al.
Publicado: (2026)

Deep Neural Networks as Iterated Function Systems and a Generalization Bound
por: Vacher, Jonathan
Publicado: (2026)

On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
por: Kobialka, Julius, et al.
Publicado: (2026)

Random Normed k-Means: A Paradigm-Shift in Clustering within Probabilistic Metric Spaces
por: Hemdanou, Abderrafik Laakel, et al.
Publicado: (2025)

Training Overparametrized Neural Networks in Sublinear Time
por: Deng, Yichuan, et al.
Publicado: (2022)

Quantitative CLTs in Deep Neural Networks
por: Favaro, Stefano, et al.
Publicado: (2023)

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
por: de Frutos, José Manuel, et al.
Publicado: (2025)

A Distributional View of High Dimensional Optimization
por: Benning, Felix
Publicado: (2025)

Consistency and Inconsistency in $K$-Means Clustering
por: Blanchard, Moïse, et al.
Publicado: (2025)

Unified Stochastic Framework for Neural Network Quantization and Pruning
por: Zhang, Haoyu, et al.
Publicado: (2024)

Neural Network Parameter-optimization of Gaussian pmDAGs
por: Saremi, Mehrzad
Publicado: (2023)

Feature Learning Dynamics in Infinite-Depth Neural Networks
por: Yao, Zihan, et al.
Publicado: (2025)

Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
por: da Cunha, Arthur, et al.
Publicado: (2023)

Importance Corrected Neural JKO Sampling
por: Hertrich, Johannes, et al.
Publicado: (2024)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
por: Tanguy, Eloi
Publicado: (2023)

Neural Diffusion Intensity Models for Point Process Data
por: Du, Xinlong, et al.
Publicado: (2026)

Eigenvalue distribution of the Neural Tangent Kernel in the quadratic scaling
por: Benigni, Lucas, et al.
Publicado: (2025)

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
por: Descours, Arnaud, et al.
Publicado: (2024)

Gaussian entropic optimal transport: Schrödinger bridges and the Sinkhorn algorithm
por: Akyildiz, O. Deniz, et al.
Publicado: (2024)

Neural stochastic Volterra equations: learning path-dependent dynamics
por: Bergerhausen, Martin, et al.
Publicado: (2024)

Why Cannot Neural Networks Master Extrapolation? Insights from Physical Laws
por: Dakhmouche, Ramzi, et al.
Publicado: (2025)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
por: Balasubramanian, Krishnakumar, et al.
Publicado: (2025)

Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
por: Miranda, Charles, et al.
Publicado: (2023)