Reverse engineering learned optimizers reveals known and novel mechanisms

4 November 2020

Papers citing "Reverse engineering learned optimizers reveals known and novel mechanisms"

10 / 10 papers shown

Title
Investigation into the Training Dynamics of Learned Optimizers Jan Sobotka Petr Simánek Daniel Vasata 26 0 0 12 Dec 2023
Improving physics-informed neural networks with meta-learned optimization Alexander Bihlo PINN 31 18 0 13 Mar 2023
VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz James Harrison C. Freeman Amil Merchant Lucas Beyer ... Naman Agrawal Ben Poole Igor Mordatch Adam Roberts Jascha Narain Sohl-Dickstein 24 60 0 17 Nov 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 44 22 0 22 Sep 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 33 32 0 22 Mar 2022
Symbolic Learning to Optimize: Towards Interpretability and Scalability Wenqing Zheng Tianlong Chen Ting-Kuei Hu Zhangyang Wang 37 18 0 13 Mar 2022
Tutorial on amortized optimization Brandon Amos OffRL 75 43 0 01 Feb 2022
Learn2Hop: Learned Optimization on Rough Landscapes Amil Merchant Luke Metz S. Schoenholz E. D. Cubuk 15 16 0 20 Jul 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 35 225 0 23 Mar 2021
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 99 1,152 0 04 Mar 2015