activity
20162022
most citedAutomatic Mixed-Precision Quantization Search of BERT

16 citations · 23 across the 6 of their papers we have counts for

collaborators

8 papers

eess.SP2023

To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Ching-Hua Lee +3

Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happ…

cs.AI20232 cited

GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer

Miao Yin, Burak Uzkent, Yilin Shen +2

The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foun…

cs.LG202220 cited

Language model compression with weighted low-rank factorization

Yen-Chang Hsu, Ting Hua, Sungen Chang +3

Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, appr…

cs.CL202116 cited

Automatic Mixed-Precision Quantization Search of BERT

Changsheng Zhao, Ting Hua, Yilin Shen +2

Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of paramet…

cs.IR2021

ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs

Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan +1

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and sati…

cs.CL2021

Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase

Akhila Yerukola, Mason Bretan, Hongxia Jin

We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare…