Cover Image

Generative Feature Training of Thin 2-Layer Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hertrich, Johannes, Neumayer, Sebastian
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms
by: Hertrich, Johannes
Published: (2024)

Fast Summation of Radial Kernels via QMC Slicing
by: Hertrich, Johannes, et al.
Published: (2024)

Numerical Methods for Kernel Slicing
by: Rux, Nicolaj, et al.
Published: (2025)

Learning Regularization Functionals for Inverse Problems: A Comparative Study
by: Hertrich, Johannes, et al.
Published: (2025)

Geometric Layer-wise Approximation Rates for Deep Networks
by: Zhang, Shijun, et al.
Published: (2026)

Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks
by: Liu, Chaoyu, et al.
Published: (2023)

A Stability Benchmark of Generative Regularizers for Inverse Problems
by: Denker, Alexander, et al.
Published: (2026)

Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
by: Hertrich, Johannes, et al.
Published: (2020)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
by: Jin, Bangti, et al.
Published: (2025)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)

A New Tensor Network: Tubal Tensor Train and Its Applications
by: Ahmadi-Asl, Salman, et al.
Published: (2026)

Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
by: Chen, Chuqi, et al.
Published: (2024)

$ε$-rank and the Staircase Phenomenon: New Insights into Neural Network Training Dynamics
by: Yang, Jiang, et al.
Published: (2024)

Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks
by: Bhandari, Diksha, et al.
Published: (2023)

Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
by: Alegría, Samuel A. Cruz, et al.
Published: (2025)

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks
by: Sun, Yuxin, et al.
Published: (2022)

Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
by: Hou, Yilong, et al.
Published: (2024)

Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding
by: Liu, Taowen, et al.
Published: (2025)

ANOVA-boosting for Random Fourier Features
by: Potts, Daniel, et al.
Published: (2024)

GenUQ: Predictive Uncertainty Estimates via Generative Hyper-Networks
by: Yen, Tian Yu, et al.
Published: (2025)

Training-Free Looped Transformers
by: Chen, Lizhang, et al.
Published: (2026)

Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
by: Mahowald, Jamie, et al.
Published: (2026)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

Cauchy Random Features for Operator Learning in Sobolev Space
by: Liao, Chunyang, et al.
Published: (2025)

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
by: Hsu, Alexander, et al.
Published: (2026)

Training Hamiltonian neural networks without backpropagation
by: Rahma, Atamert, et al.
Published: (2024)

Numerical Fragility in Transformers: A Layer-wise Theory for Explaining, Forecasting, and Mitigating Instability
by: Baek, Jinwoo
Published: (2025)

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
by: Andre-Sloan, Sebastien, et al.
Published: (2026)

Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
by: Rusch, T. Konstantin, et al.
Published: (2024)

Multi-Level Monte Carlo Training of Neural Operators
by: Rowbottom, James, et al.
Published: (2025)

Multi-Preconditioned LBFGS for Training Finite-Basis PINNs
by: Salvadó-Benasco, Marc, et al.
Published: (2026)

Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
by: Esche, Sebastian, et al.
Published: (2025)

Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives
by: Wagner, Theresa, et al.
Published: (2024)

Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
by: Chaudhry, Faris
Published: (2026)

Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition
by: Chen, Yuanhong, et al.
Published: (2025)

A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection
by: Shao, Yu, et al.
Published: (2024)

Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
by: Chang, Che-Chia, et al.
Published: (2026)

Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training
by: Wang, Hong, et al.
Published: (2025)

FastLSQ: Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives
by: Sulc, Antonin
Published: (2026)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
by: Yang, Yahong
Published: (2024)