research
∙
05/07/2023
Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
Transformer models are foundational to natural language processing (NLP)...
share
research
∙
01/18/2022
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
A fundamental challenge in Bayesian inference is efficient representatio...
research
∙
11/02/2021
Low-Rank+Sparse Tensor Compression for Neural Networks
Low-rank tensor compression has been proposed as a promising approach to...
research
∙
10/12/2021
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Consistency training is a popular method to improve deep learning models...
research
∙
05/11/2021
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
The deep neural network (DNN) based AI applications on the edge require ...
research
∙
04/07/2021
On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method
Various hardware accelerators have been developed for energy-efficient a...
research
∙
10/17/2020
End-to-End Variational Bayesian Training of Tensorized Neural Networks with Automatic Rank Determination
Low-rank tensor decomposition is one of the most effective approaches to...
research
∙
05/24/2019
Bayesian Tensorized Neural Networks with Automatic Rank Selection
Tensor decomposition is an effective approach to compress over-parameter...
research
∙
09/06/2018