Prioritized Training on Points that are Learnable, Worth Learning, and
Not Yet Learnt

Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt

14 June 2022

Sören Mindermann

Muhammed Razzak

Benedikt Höltgen

Sebastian Farquhar

Papers citing "Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt"

12 / 112 papers shown

Title
The Impact of Data Corruption on Named Entity Recognition for Low-resourced Languages Manuel A. Fokam Michael Beukman 19 0 0 09 Aug 2022
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities Andreas Kirsch Y. Gal FedML 22 21 0 01 Aug 2022
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling Andreas Kirsch Jannik Kossen Y. Gal UQCV BDL 34 3 0 18 May 2022
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition Abdul Hameed Azeemi I. Qazi Agha Ali Raza 13 0 0 18 Mar 2022
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions Yufeng Wang Dan Li Cong Xu Min Yang 14 0 0 13 Feb 2022
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version) Sören Mindermann Muhammed Razzak Winnie Xu Andreas Kirsch Mrinank Sharma Adrien Morisot Aidan N. Gomez Sebastian Farquhar J. Brauner Y. Gal 14 6 0 06 Jul 2021
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 243 1,815 0 17 Sep 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,187 0 23 Aug 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,943 0 20 Apr 2018
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 267 404 0 09 Apr 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,878 0 15 Sep 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015