:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Mufan Bill, Nica, Mihai
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2310.12079
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Network Degeneracy as an Indicator of Training Performance: Comparing Finite and Infinite Width Angle Predictions
by: Jakub, Cameron, et al.
Published: (2023)

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks
by: Stupariu, Teodor-Mihai, et al.
Published: (2026)

Neural Variable-Order Fractional Differential Equation Networks
by: Cui, Wenjun, et al.
Published: (2025)

Sampling from the Mean-Field Stationary Distribution
by: Kook, Yunbum, et al.
Published: (2024)

Analysis of Langevin Monte Carlo from Poincaré to Log-Sobolev
by: Chewi, Sinho, et al.
Published: (2021)

Dynamic Sparse Training with Structured Sparsity
by: Lasby, Mike, et al.
Published: (2023)

On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
by: Eliasof, Moshe, et al.
Published: (2024)

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

Correctness Verification of Neural Networks Approximating Differential Equations
by: Ellinas, Petros, et al.
Published: (2024)

Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
by: Hong, Sanghyun, et al.
Published: (2025)

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
by: Gupta, Aarush, et al.
Published: (2025)

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
by: Bergna, Richard, et al.
Published: (2024)

PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
by: Matthews, Jason, et al.
Published: (2024)

Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
by: Ben-Yair, Ido, et al.
Published: (2021)

Acceleration of Gossip Algorithms through the Euler-Poisson-Darboux Equation
by: Berthier, Raphaël, et al.
Published: (2022)

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

Continuum Dropout for Neural Differential Equations
by: Lee, Jonghun, et al.
Published: (2025)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
by: Chen, Jiale, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs
by: Martinet, Eloi, et al.
Published: (2025)

Uncertainty Propagation Networks for Neural Ordinary Differential Equations
by: Jahanshahi, Hadi, et al.
Published: (2025)

Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
by: Yue, Juwei, et al.
Published: (2025)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
by: Sun, Jingbo, et al.
Published: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026)

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
by: Thöni, Anna C. M., et al.
Published: (2025)

ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Data
by: Wang, Haoling, et al.
Published: (2026)

Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
by: Dang, Haoning, et al.
Published: (2026)

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
by: Yin, Pengsong, et al.
Published: (2024)

A Universal Model Combining Differential Equations and Neural Networks for Ball Trajectory Prediction
by: Shi, Zhiwei, et al.
Published: (2025)

Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network
by: Lin, Zhongshuo, et al.
Published: (2025)

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
by: Yang, Qihong, et al.
Published: (2026)

Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers
by: Li, Shi
Published: (2025)

Latent Variable Sequence Identification for Cognitive Models with Neural Network Estimators
by: Pan, Ti-Fen, et al.
Published: (2024)

Fourier-Enhanced Recurrent Neural Networks for Electrical Load Time Series Downscaling
by: Chen, Qi, et al.
Published: (2025)

Temporal Motif Signatures for Temporal Graph Neural Networks
by: Sandfelder, Dylan, et al.
Published: (2026)

On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
by: Bleistein, Linus, et al.
Published: (2023)

Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
by: Nagesh, Chandra Kanth, et al.
Published: (2025)