Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions

24 October 2023

Papers citing "Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions"

6 / 6 papers shown

Title
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization Ali Behrouz Meisam Razaviyayn Peilin Zhong Vahab Mirrokni 38 0 0 17 Apr 2025
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues Riccardo Grazzi Julien N. Siems Jörg K.H. Franke Arber Zela Frank Hutter Massimiliano Pontil 92 11 0 19 Nov 2024
Autoregressive Large Language Models are Computationally Universal Dale Schuurmans Hanjun Dai Francesco Zanini 33 2 0 04 Oct 2024
Rethinking Transformers in Solving POMDPs Chenhao Lu Ruizhe Shi Yuyao Liu Kaizhe Hu Simon S. Du Huazhe Xu AI4CE 27 3 0 27 May 2024
Exploring the Promise and Limits of Real-Time Recurrent Learning Kazuki Irie Anand Gopalakrishnan Jürgen Schmidhuber 19 15 0 30 May 2023
Neural Networks and the Chomsky Hierarchy Grégoire Delétang Anian Ruoss Jordi Grau-Moya Tim Genewein L. Wenliang ... Chris Cundy Marcus Hutter Shane Legg Joel Veness Pedro A. Ortega UQCV 101 130 0 05 Jul 2022