On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools

25 June 2020

Papers citing "On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools"

8 / 8 papers shown

Title
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning Anh Tong Thanh Nguyen-Tang Dongeun Lee Duc Nguyen Toan M. Tran David Hall Cheongwoong Kang Jaesik Choi 87 1 0 03 Mar 2025
Hierarchy of chaotic dynamics in random modular networks Łukasz Kuśmierz Ulises Pereira-Obilinovic Zhixin Lu Dana Mastrovito Stefan Mihalas 26 0 0 08 Oct 2024
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics Giancarlo Kerg Kyle Goyette M. P. Touzel Gauthier Gidel Eugene Vorontsov Yoshua Bengio Guillaume Lajoie 26 58 0 28 May 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 69 286 0 13 Feb 2019
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs D. Gilboa B. Chang Minmin Chen Greg Yang S. Schoenholz Ed H. Chi Jeffrey Pennington 53 41 0 25 Jan 2019
Exponential expressivity in deep neural networks through transient chaos Ben Poole Subhaneil Lahiri M. Raghu Jascha Narain Sohl-Dickstein Surya Ganguli 76 587 0 16 Jun 2016
Recurrent Orthogonal Networks and Long-Memory Tasks Mikael Henaff Arthur Szlam Yann LeCun 41 131 0 22 Feb 2016
Visualizing and Understanding Recurrent Networks A. Karpathy Justin Johnson Li Fei-Fei HAI 74 1,100 0 05 Jun 2015