102 citations · 196 across the 10 of their papers we have counts for
5 papers · 1 filter
Understanding the Effect of the Long Tail on Neural Network Compression
Harvey Dam, Vinu Joseph, Aditya Bhaskara +3
Network compression is now a mature sub-field of neural network research: over the last decade, significant progress has been made towards reducing the size of models and speeding…
Efficient Sparsely Activated Transformers
Salar Latifi, Saurav Muralidharan, Michael Garland
Transformer-based neural networks have achieved state-of-the-art task performance in a number of machine learning domains including natural language processing and computer vision.…
A Programmable Approach to Neural Network Compression
Vinu Joseph, Saurav Muralidharan, Animesh Garg +2
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Conse…
Accelerating Reinforcement Learning through GPU Atari Emulation
Steven Dalton, Iuri Frosio, Michael Garland
We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari Learning Environment (ALE) which is used for the development of deep reinforcement algorithms. CuLE overcome…
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Aditya Devarakonda, Maxim Naumov, Michael Garland
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size. While smaller batch sizes generally c…