13 citations · 28 across the 6 of their papers we have counts for
6 papers
Boost Neural Networks by Checkpoints
Feng Wang, Guoyizhe Wei, Qiao Liu +3
Training multiple deep neural networks (DNNs) and averaging their outputs is a simple way to improve the predictive performance. Nevertheless, the multiplied training cost prevents…
Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models
Coleman Hooper, Thierry Tambe, Gu-Yeon Wei
This work analyzes how attention-based Bidirectional Long Short-Term Memory (BLSTM) models adapt to noise-augmented speech. We identify crucial components for noise adaptation in B…
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Thierry Tambe, Coleman Hooper, Lillian Pentecost +8
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computation…
Learning Low-Rank Approximation for CNNs
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +1
Low-rank approximation is an effective model compression technique to not only reduce parameter storage requirements, but to also reduce computations. For convolutional neural netw…
Network Pruning for Low-Rank Binary Indexing
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +2
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs). Computations using sparse matrices obtained by pruning…
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization
Se Jung Kwon, Dongsoo Lee, Byeongwook Kim +3
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model si…