Cover Image

Geometry and Local Recovery of Global Minima of Two-layer Neural Networks at Overparameterization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Leyang, Zhang, Yaoyu, Luo, Tao
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Dynamical Systems
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
by: Zhang, Yaoyu, et al.
Published: (2024)

Geometry of Critical Sets and Existence of Saddle Branches for Two-layer Neural Networks
by: Zhang, Leyang, et al.
Published: (2024)

Uncovering Critical Sets of Deep Neural Networks via Sample-Independent Critical Lifting
by: Zhang, Leyang, et al.
Published: (2025)

Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning
by: Chemnitz, Dennis, et al.
Published: (2024)

Generalized Lagrangian Neural Networks
by: Xiao, Shanshan, et al.
Published: (2024)

On Interpolation Formulas Describing Neural Network Generalization
by: Guo, Jin, et al.
Published: (2026)

A Novel Neural Filter to Improve Accuracy of Neural Network Models of Dynamic Systems
by: Oveissi, Parham, et al.
Published: (2024)

Absence of Closed-Form Descriptions for Gradient Flow in Two-Layer Narrow Networks
by: Park, Yeachan
Published: (2024)

PINN-Obs: Physics-Informed Neural Network-Based Observer for Nonlinear Dynamical Systems
by: Farkane, Ayoub, et al.
Published: (2025)

Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
by: Caralt, Ferran Hernandez, et al.
Published: (2024)

Time-Scale Coupling Between States and Parameters in Recurrent Neural Networks
by: Livi, Lorenzo
Published: (2025)

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
by: Chen, Chen, et al.
Published: (2025)

Gradient Flow Equations for Deep Linear Neural Networks: A Survey from a Network Perspective
by: Wendin, Joel, et al.
Published: (2025)

GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations
by: Forootani, Ali, et al.
Published: (2024)

Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks
by: Kneifl, Jonas, et al.
Published: (2024)

Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics
by: Herz, Andre, et al.
Published: (2026)

Predator Prey Scavenger Model using Holling's Functional Response of Type III and Physics-Informed Deep Neural Networks
by: Panchal, Aneesh, et al.
Published: (2024)

State Estimation Using Sparse DEIM and Recurrent Neural Networks
by: Farazmand, Mohammad
Published: (2024)

Symbolic Regression via Neural Networks
by: Boddupalli, Nibodh, et al.
Published: (2026)

Backpropagation on Dynamical Networks
by: Tan, Eugene, et al.
Published: (2022)

Learning the Simplest Neural ODE
by: Okamoto, Yuji, et al.
Published: (2025)

Signature Maximum Mean Discrepancy Two-Sample Statistical Tests
by: Alden, Andrew, et al.
Published: (2025)

Upper Generalization Bounds for Neural Oscillators
by: Huang, Zifeng, et al.
Published: (2026)

Equivariant Manifold Neural ODEs and Differential Invariants
by: Andersdotter, Emma, et al.
Published: (2024)

A Dynamical Systems Perspective on the Analysis of Neural Networks
by: Chemnitz, Dennis, et al.
Published: (2025)

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
by: Wang, Shida, et al.
Published: (2023)

Neural Context Flows for Meta-Learning of Dynamical Systems
by: Nzoyem, Roussel Desmond, et al.
Published: (2024)

Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics
by: Smart, Matthew, et al.
Published: (2026)

Cross-Modal Diffusion for Biomechanical Dynamical Systems Through Local Manifold Alignment
by: Dey, Sharmita, et al.
Published: (2025)

Enhancing Neural Training via a Correlated Dynamics Model
by: Brokman, Jonathan, et al.
Published: (2023)

Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics
by: Samota, Sandeep Kumar, et al.
Published: (2026)

Conformal Disentanglement: A Neural Framework for Perspective Synthesis and Differentiation
by: Kevrekidis, George A., et al.
Published: (2024)

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
by: Samota, Sandeep Kumar, et al.
Published: (2026)

Architecture Induces Structural Invariant Manifolds of Neural Network Training Dynamics
by: Zhao, Jiajie, et al.
Published: (2025)

MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
by: Yang, Lake, et al.
Published: (2026)

Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations
by: van Tegelen, Eva, et al.
Published: (2025)

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
by: Li, Xuyang, et al.
Published: (2025)

Transmission Neural Networks: Inhibitory and Excitatory Connections
by: Gao, Shuang, et al.
Published: (2026)

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
by: Bhan, Luke, et al.
Published: (2025)

Reduced Order Modeling with Shallow Recurrent Decoder Networks
by: Tomasetto, Matteo, et al.
Published: (2025)