Framing RNN as a kernel method: A neural ODE approach

2 June 2021

Papers citing "Framing RNN as a kernel method: A neural ODE approach"

8 / 8 papers shown

Title
Theoretical Foundations of Deep Selective State-Space Models Nicola Muca Cirone Antonio Orvieto Benjamin Walker C. Salvi Terry Lyons Mamba 53 25 0 29 Feb 2024
PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs Dániel Rácz M. Petreczky Bálint Daróczy 55 0 0 07 Jul 2023
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations Linus Bleistein Agathe Guilloux 30 1 0 26 May 2023
Learning the Dynamics of Sparsely Observed Interacting Systems Linus Bleistein Adeline Fermanian A. Jannot Agathe Guilloux 38 5 0 27 Jan 2023
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening Martin Gonzalez H. Hajri Loic Cantat M. Petreczky 27 1 0 16 Jun 2022
Scaling ResNets in the Large-depth Regime P. Marion Adeline Fermanian Gérard Biau Jean-Philippe Vert 26 16 0 14 Jun 2022
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules Kazuki Irie Francesco Faccio Jürgen Schmidhuber AI4TS 27 11 0 03 Jun 2022
Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings Martin Gonzalez Thibault Defourneau H. Hajri M. Petreczky 31 2 0 24 May 2022