research
∙
08/03/2023
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
Obtaining versions of deep neural networks that are both highly-accurate...
share
research
∙
05/27/2023
Knowledge Distillation Performs Partial Variance Reduction
Knowledge distillation is a popular approach for enhancing the performan...
research
∙
04/25/2023
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures
Pruning - that is, setting a significant subset of the parameters of a n...
research
∙
07/28/2022
CrAM: A Compression-Aware Minimizer
We examine the question of whether SGD-based optimization of deep neural...
research
∙
11/26/2021
How Well Do Sparse Imagenet Models Transfer?
Transfer learning is a classic paradigm by which models pretrained on la...
research
∙
07/08/2021
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models
The availability of large amounts of user-provided data has been key to ...
research
∙
06/23/2021
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
The increasing computational requirements of deep neural networks (DNNs)...
research
∙
01/31/2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven th...
research
∙
07/05/2018
