DeepOBS: A Deep Learning Optimizer Benchmark Suite

13 March 2019

Papers citing "DeepOBS: A Deep Learning Optimizer Benchmark Suite"

10 / 10 papers shown

Title
Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW Sourabh Medapati Priya Kasimbeg Shankar Krishnan Naman Agarwal George E. Dahl 57 0 0 06 Mar 2025
Learning Versatile Optimizers on a Compute Diet A. Moudgil Boris Knyazev Guillaume Lajoie Eugene Belilovsky 135 0 0 22 Jan 2025
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework Esteban Garces Arias Hannah Blocher Julian Rodemann Meimingwei Li Christian Heumann Matthias Aßenmacher 25 1 0 24 Oct 2024
Debiasing Mini-Batch Quadratics for Applications in Deep Learning Lukas Tatzel Bálint Mucsányi Osane Hackel Philipp Hennig 43 0 0 18 Oct 2024
Deconstructing What Makes a Good Optimizer for Language Models Rosie Zhao Depen Morwani David Brandfonbrener Nikhil Vyas Sham Kakade 42 17 0 10 Jul 2024
HesScale: Scalable Computation of Hessian Diagonals Mohamed Elsayed A. R. Mahmood 14 7 0 20 Oct 2022
A Stochastic Bundle Method for Interpolating Networks Alasdair Paren Leonard Berrada Rudra P. K. Poudel M. P. Kumar 24 4 0 29 Jan 2022
Large-Scale Deep Learning Optimizations: A Comprehensive Survey Xiaoxin He Fuzhao Xue Xiaozhe Ren Yang You 22 14 0 01 Nov 2021
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen Dami Choi Lukas Balles D. Duvenaud Philipp Hennig ODL 30 6 0 09 Nov 2020
L4: Practical loss-based stepsize adaptation for deep learning Michal Rolínek Georg Martius ODL 36 63 0 14 Feb 2018