29 citations · 60 across the 9 of their papers we have counts for
9 papers
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation
Minghao Chen, Zepeng Gao, Shuai Zhao +4
Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set…
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Peiyu Liu, Zikang Liu, Ze-Feng Gao +5
Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…
Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
Peiyu Liu, Ze-Feng Gao, Yushuo Chen +2
In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters…
Decentralized, not Dehumanized in the Metaverse: Bringing Utility to NFTs through Multimodal Interaction
Anqi Wang, Ze Gao, Lik-Hang Lee +2
User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect…
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
Ze-Feng Gao, Peiyu Liu, Wayne Xin Zhao +2
Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models. However, MoE requires inco…
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
Peiyu Liu, Ze-Feng Gao, Wayne Xin Zhao +3
This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. It can decomp…
Compressing LSTM Networks by Matrix Product Operators
Ze-Feng Gao, Xingwei Sun, Lan Gao +2
Long Short Term Memory(LSTM) models are the building blocks of many state-of-the-art natural language processing(NLP) and speech enhancement(SE) algorithms. However, there are a la…
A Model Compression Method with Matrix Product Operators for Speech Enhancement
Xingwei Sun, Ze-Feng Gao, Zhong-Yi Lu +2
The deep neural network (DNN) based speech enhancement approaches have achieved promising performance. However, the number of parameters involved in these methods is usually enormo…
Compressing deep neural networks by matrix product operators
Ze-Feng Gao, Song Cheng, Rong-Qiang He +4
A deep neural network is a parametrization of a multilayer mapping of signals in terms of many alternatively arranged linear and nonlinear transformations. The linear transformatio…