Ensembles provably learn equivariance through data augmentation
Fuente:
arXiv
Saved in:
| Main Authors: | Nordenfors, Oskar, Flinth, Axel |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data Augmentation and Regularization for Learning Group Equivariance
by: Nordenfors, Oskar, et al.
Published: (2025)
by: Nordenfors, Oskar, et al.
Published: (2025)
Optimization Dynamics of Equivariant and Augmented Neural Networks
by: Nordenfors, Oskar, et al.
Published: (2023)
by: Nordenfors, Oskar, et al.
Published: (2023)
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
by: Wang, Yueyang, et al.
Published: (2026)
by: Wang, Yueyang, et al.
Published: (2026)
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems
by: Tonini, Andrea, et al.
Published: (2025)
by: Tonini, Andrea, et al.
Published: (2025)
Energy Dissipation Rate Guided Adaptive Sampling for Physics-Informed Neural Networks: Resolving Surface-Bulk Dynamics in Allen-Cahn Systems
by: Li, Chunyan, et al.
Published: (2025)
by: Li, Chunyan, et al.
Published: (2025)
A scaled TW-PINN: A physics-informed neural network for traveling wave solutions of reaction-diffusion equations with general coefficients
by: Han, Seungwan, et al.
Published: (2026)
by: Han, Seungwan, et al.
Published: (2026)
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations
by: Ackermann, Julia, et al.
Published: (2024)
by: Ackermann, Julia, et al.
Published: (2024)
A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)
by: Kapllani, Lorenc, et al.
Published: (2024)
A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)
by: Kapllani, Lorenc, et al.
Published: (2024)
Error estimates of asymptotic-preserving neural networks in approximating stochastic linearized Boltzmann equation
by: Wan, Jiayu, et al.
Published: (2025)
by: Wan, Jiayu, et al.
Published: (2025)
The learned range test method for the inverse inclusion problem
by: Sun, Shiwei, et al.
Published: (2024)
by: Sun, Shiwei, et al.
Published: (2024)
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
by: Ackermann, Julia, et al.
Published: (2023)
by: Ackermann, Julia, et al.
Published: (2023)
Adaptive recurrent flow map operator learning for reaction diffusion dynamics
by: Tunc, Huseyin
Published: (2026)
by: Tunc, Huseyin
Published: (2026)
Solving the Fisher nonlinear differential equations via Physics-Informed Neural Networks: A Comprehensive Retraining Study and Comparative Analysis with the Finite Difference Method
by: Aberqi, Ahmed, et al.
Published: (2026)
by: Aberqi, Ahmed, et al.
Published: (2026)
Solving the BGK Model and Boltzmann equation by Fourier Neural Operator with conservative constraints
by: Hu, Boyun, et al.
Published: (2025)
by: Hu, Boyun, et al.
Published: (2025)
Physics-informed approach for exploratory Hamilton--Jacobi--Bellman equations via policy iterations
by: Kim, Yeongjong, et al.
Published: (2025)
by: Kim, Yeongjong, et al.
Published: (2025)
A posteriori certification for neural network approximations to PDEs
by: Ernst, Lewin, et al.
Published: (2025)
by: Ernst, Lewin, et al.
Published: (2025)
Recurrent Neural Operators: Stable Long-Term PDE Prediction
by: Ye, Zaijun, et al.
Published: (2025)
by: Ye, Zaijun, et al.
Published: (2025)
Automated Code Generation and Validation for Software Components of Microcontrollers
by: Haug, Sebastian, et al.
Published: (2025)
by: Haug, Sebastian, et al.
Published: (2025)
Improved bounds for randomized Schatten norm estimation of numerically low-rank matrices
by: Chu, Ya-Chi, et al.
Published: (2024)
by: Chu, Ya-Chi, et al.
Published: (2024)
Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
by: Cohen, Samuel N., et al.
Published: (2025)
by: Cohen, Samuel N., et al.
Published: (2025)
Score-based constrained generative modeling via Langevin diffusions with boundary conditions
by: Nordenhög, Adam, et al.
Published: (2025)
by: Nordenhög, Adam, et al.
Published: (2025)
A short tour of operator learning theory: Convergence rates, statistical limits, and open questions
by: Brugiapaglia, Simone, et al.
Published: (2026)
by: Brugiapaglia, Simone, et al.
Published: (2026)
Single-shot prediction of parametric partial differential equations
by: Rafiq, Khalid, et al.
Published: (2025)
by: Rafiq, Khalid, et al.
Published: (2025)
Embedding Inequalities for Barron-type Spaces
by: Wu, Lei
Published: (2023)
by: Wu, Lei
Published: (2023)
Approximation theory for 1-Lipschitz ResNets
by: Murari, Davide, et al.
Published: (2025)
by: Murari, Davide, et al.
Published: (2025)
Numerical PDE solvers outperform neural PDE solvers
by: Chatain, Patrick, et al.
Published: (2025)
by: Chatain, Patrick, et al.
Published: (2025)
Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation
by: Oh, Jaemin, et al.
Published: (2024)
by: Oh, Jaemin, et al.
Published: (2024)
On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures
by: Lin, Likun, et al.
Published: (2026)
by: Lin, Likun, et al.
Published: (2026)
A Physics-Informed, Global-in-Time Neural Particle Method for the Spatially Homogeneous Landau Equation
by: Kim, Minseok, et al.
Published: (2026)
by: Kim, Minseok, et al.
Published: (2026)
Physics-informed features in supervised machine learning
by: Lampani, Margherita, et al.
Published: (2025)
by: Lampani, Margherita, et al.
Published: (2025)
Learning to Control the Smoothness of Graph Convolutional Network Features
by: Wang, Shih-Hsin, et al.
Published: (2024)
by: Wang, Shih-Hsin, et al.
Published: (2024)
Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality
by: Jentzen, Arnulf, et al.
Published: (2025)
by: Jentzen, Arnulf, et al.
Published: (2025)
Edge-Wise Graph-Instructed Neural Networks
by: Della Santa, Francesco, et al.
Published: (2024)
by: Della Santa, Francesco, et al.
Published: (2024)
A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
by: Chen, Gaohang, et al.
Published: (2025)
by: Chen, Gaohang, et al.
Published: (2025)
Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
by: Shin, Jin Young, et al.
Published: (2022)
by: Shin, Jin Young, et al.
Published: (2022)
Neural Shape Operator Surrogates -- Expression Rate Bounds
by: Harbrecht, Helmut, et al.
Published: (2026)
by: Harbrecht, Helmut, et al.
Published: (2026)
A 3D Machine Learning based Volume Of Fluid scheme without explicit interface reconstruction
by: Pintore, Moreno, et al.
Published: (2025)
by: Pintore, Moreno, et al.
Published: (2025)
Neural Discovery of Strichartz Extremizers
by: Valenzuela, Nicolás, et al.
Published: (2026)
by: Valenzuela, Nicolás, et al.
Published: (2026)
In-Context Operator Learning on the Space of Probability Measures
by: Cole, Frank, et al.
Published: (2026)
by: Cole, Frank, et al.
Published: (2026)
Similar Items
-
Data Augmentation and Regularization for Learning Group Equivariance
by: Nordenfors, Oskar, et al.
Published: (2025) -
Optimization Dynamics of Equivariant and Augmented Neural Networks
by: Nordenfors, Oskar, et al.
Published: (2023) -
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
by: Wang, Yueyang, et al.
Published: (2026) -
Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems
by: Tonini, Andrea, et al.
Published: (2025) -
Energy Dissipation Rate Guided Adaptive Sampling for Physics-Informed Neural Networks: Resolving Surface-Bulk Dynamics in Allen-Cahn Systems
by: Li, Chunyan, et al.
Published: (2025)