Copertina

On The Convergence of Euler Discretization of Finite-Time Convergent Gradient Flows

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Siqi, Benosman, Mouhacine, Romero, Orlando
Natura:	Preprint
Pubblicazione:	2020
Soggetti:	Machine Learning Systems and Control 68T07
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets
di: Hoseinpour, Milad, et al.
Pubblicazione: (2025)

Reinforcement learning-based estimation for partial differential equations
di: Mowlavi, Saviz, et al.
Pubblicazione: (2023)

Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies
di: Chizat, Lénaïc, et al.
Pubblicazione: (2026)

Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence
di: Castin, Valérie, et al.
Pubblicazione: (2026)

Convergence of Continuous Normalizing Flows for Learning Probability Distributions
di: Gao, Yuan, et al.
Pubblicazione: (2024)

Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction
di: Li, Ruiyuan, et al.
Pubblicazione: (2026)

Learning Sampled-data Control for Swarms via MeanFlow
di: Dong, Anqi, et al.
Pubblicazione: (2026)

Generative modeling of conditional probability distributions on the level-sets of collective variables
di: Akhyar, Fatima-Zahrae, et al.
Pubblicazione: (2025)

Directional Convergence, Benign Overfitting of Gradient Descent in leaky ReLU two-layer Neural Networks
di: Hashimoto, Ichiro
Pubblicazione: (2025)

Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling
di: Ni, Zhongjun, et al.
Pubblicazione: (2024)

Exact Sequence Interpolation with Transformers
di: Alcalde, Albert, et al.
Pubblicazione: (2025)

On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
di: Štrupl, Miroslav, et al.
Pubblicazione: (2025)

Input Convex Kolmogorov Arnold Networks
di: Deschatre, Thomas, et al.
Pubblicazione: (2025)

Nesterov acceleration despite very noisy gradients
di: Gupta, Kanan, et al.
Pubblicazione: (2023)

Data-Driven Approach for Accelerating Selective Harmonic Elimination Algorithm in Parallel Power Converters
di: Karimi, E., et al.
Pubblicazione: (2024)

A Generalization Bound for a Family of Implicit Networks
di: Fung, Samy Wu, et al.
Pubblicazione: (2024)

Safety Margins for Reinforcement Learning
di: Grushin, Alexander, et al.
Pubblicazione: (2023)

Criticality and Safety Margins for Reinforcement Learning
di: Grushin, Alexander, et al.
Pubblicazione: (2024)

Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition
di: Tepakbong, Nathanael, et al.
Pubblicazione: (2025)

Non-Asymptotic Convergence of Discrete Diffusion Models: Masked and Random Walk dynamics
di: Conforti, Giovanni, et al.
Pubblicazione: (2025)

Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
di: Zhang, Xiangyuan, et al.
Pubblicazione: (2023)

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
di: Yi, Zeji, et al.
Pubblicazione: (2026)

Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System
di: Kang, Shinhoo, et al.
Pubblicazione: (2026)

BP(λ): Online Learning via Synthetic Gradients
di: Pemberton, Joseph, et al.
Pubblicazione: (2024)

Convergence of gradient descent for deep neural networks
di: Chatterjee, Sourav
Pubblicazione: (2022)

Flow matching on homogeneous spaces
di: Ruscelli, Francesco
Pubblicazione: (2026)

Tracking Finite-Time Lyapunov Exponents to Robustify Neural ODEs
di: Wöhrer, Tobias, et al.
Pubblicazione: (2026)

Graph-Conditional Flow Matching for Relational Data Generation
di: Scassola, Davide, et al.
Pubblicazione: (2025)

Generative Design of Ship Propellers using Conditional Flow Matching
di: Kruger, Patrick, et al.
Pubblicazione: (2026)

Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality
di: Ged, François, et al.
Pubblicazione: (2023)

Tracking the Median of Gradients with a Stochastic Proximal Point Method
di: Schaipp, Fabian, et al.
Pubblicazione: (2024)

Diagonal Linear Networks and the Lasso Regularization Path
di: Berthier, Raphaël
Pubblicazione: (2025)

Explicit neural network classifiers for non-separable data
di: Ewald, Patrícia Muñoz
Pubblicazione: (2025)

An Improved Adaptive PID Optimizer with Enhanced Convergence and Stability for Deep Learning
di: Saini, Saurabh, et al.
Pubblicazione: (2026)

Convergence of the Stochastic Heavy Ball Method With Approximate Gradients and/or Block Updating
di: Tadipatri, Uday Kiran Reddy, et al.
Pubblicazione: (2023)

Clustering in pure-attention hardmax transformers and its role in sentiment analysis
di: Alcalde, Albert, et al.
Pubblicazione: (2024)

Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
di: Jeong, Halyun, et al.
Pubblicazione: (2025)

On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime
di: Jiang, Shuai, et al.
Pubblicazione: (2026)

Gradient descent provably escapes saddle points in the training of shallow ReLU networks
di: Cheridito, Patrick, et al.
Pubblicazione: (2022)

Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks
di: Lam, Samuel Chun-Hei, et al.
Pubblicazione: (2025)