Using learned optimizers to make models robust to input noise

Using learned optimizers to make models robust to input noise

8 June 2019

Niru Maheswaranathan

Jonathon Shlens

Jascha Narain Sohl-Dickstein

Papers citing "Using learned optimizers to make models robust to input noise"

15 / 15 papers shown

Title
Enhancing object detection robustness: A synthetic and natural perturbation approach N. Premakumara B. Jalaeian N. Suri H. Samani 26 3 0 20 Apr 2023
Mnemosyne: Learning to Train Transformers with Transformers Deepali Jain K. Choromanski Kumar Avinava Dubey Sumeet Singh Vikas Sindhwani Tingnan Zhang Jie Tan OffRL 39 9 0 02 Feb 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 35 60 0 17 Nov 2022
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases James Harrison Luke Metz Jascha Narain Sohl-Dickstein 47 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 38 32 0 22 Mar 2022
Tutorial on amortized optimization Brandon Amos OffRL 75 43 0 01 Feb 2022
Neural Fixed-Point Acceleration for Convex Optimization Shobha Venkataraman Brandon Amos 38 13 0 21 Jul 2021
Learn2Hop: Learned Optimization on Rough Landscapes Amil Merchant Luke Metz S. Schoenholz E. D. Cubuk 31 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 52 225 0 23 Mar 2021
Training Learned Optimizers with Randomly Initialized Learned Optimizers Luke Metz C. Freeman Niru Maheswaranathan Jascha Narain Sohl-Dickstein 43 12 0 14 Jan 2021
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves Luke Metz Niru Maheswaranathan C. Freeman Ben Poole Jascha Narain Sohl-Dickstein 33 62 0 23 Sep 2020
learn2learn: A Library for Meta-Learning Research Sébastien M. R. Arnold Praateek Mahajan Debajyoti Datta Ian Bunner Konstantinos Saitas Zarkias 25 95 0 27 Aug 2020
Using a thousand optimization tasks to learn hyperparameter search strategies Luke Metz Niru Maheswaranathan Ruoxi Sun C. Freeman Ben Poole Jascha Narain Sohl-Dickstein 20 45 0 27 Feb 2020
When MAML Can Adapt Fast and How to Assist When It Cannot Sébastien M. R. Arnold Shariq Iqbal Fei Sha 19 5 0 30 Oct 2019
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks Hang Yu Aishan Liu Xianglong Liu Gen Li Ping Luo R. Cheng Jichen Yang Chongzhi Zhang AAML 34 10 0 11 Sep 2019