Recurrent neural networks: vanishing and exploding gradients are not the
end of the story

Recurrent neural networks: vanishing and exploding gradients are not the end of the story

31 May 2024

Nicolas Zucchet

Antonio Orvieto

ArXiv (abs)PDF HTML

Papers citing "Recurrent neural networks: vanishing and exploding gradients are not the end of the story"

7 / 7 papers shown

Title
Weight-Space Linear Recurrent Neural Networks Roussel Desmond Nzoyem Nawid Keshtmand Idriss Tsayem David A.W. Barton Tom Deakin 32 0 0 01 Jun 2025
In Search of Adam's Secret Sauce Antonio Orvieto Robert Gower 25 1 0 27 May 2025
Revisiting Glorot Initialization for Long-Range Linear Recurrences Noga Bar Mariia Seleznova Yotam Alexander Gitta Kutyniok Raja Giryes 12 0 0 26 May 2025
Scalable Graph Generative Modeling via Substructure Sequences Zehong Wang Zheyuan Zhang Tianyi Ma Chuxu Zhang Yanfang Ye AI4CE 63 0 0 22 May 2025
Dynamically Learning to Integrate in Recurrent Neural Networks Blake Bordelon Jordan Cotler Cengiz Pehlevan Jacob A. Zavatone-Veth 96 3 0 24 Mar 2025
Fixed-Point RNNs: From Diagonal to Dense in a Few Iterations Sajad Movahedi Felix Sarnthein Nicola Muca Cirone Antonio Orvieto 103 5 0 13 Mar 2025
Theoretical Foundations of Deep Selective State-Space Models Nicola Muca Cirone Antonio Orvieto Benjamin Walker C. Salvi Terry Lyons Mamba 274 34 0 29 Feb 2024

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