117 citations · 357 across the 10 of their papers we have counts for
10 papers
Nanodevice-Enabled Near-Field Thermal Radiation between Sub-Wavelength Surfaces
Xiao Luo, Hakan Salihoglu, Zexiao Wang +6
With the continuous advancement of nanotechnology, nanodevices have become crucial components in computing, sensing and energy conversion applications. However, the structures of n…
Unsupervised machine learning for identifying phase transition using two-times clustering
Nan Wu, Zhuohan Li, Wanzhou Zhang
In recent years, developing unsupervised machine learning for identifying phase transition is a research direction. In this paper, we introduce a two-times clustering method that c…
Design and analysis of electrothermal metasurfaces
Xiu Liu, Zhuo Li, Zexiao Wang +2
Electrothermal metasurfaces have attracted extensive attention due to their ability to dynamically control thermal infrared radiation. Although previous studies were mainly focused…
Physical Origin of the Mechanochemical Coupling at Interfaces
Zhuohan Li, Izabela Szlufarska
We used density functional theory (DFT) calculations to investigate the physical origin of the mechano-chemical response of materials interfaces. Our results show that the mechano-…
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
Zhuohan Li, Siyuan Zhuang, Shiyuan Guo +4
Model parallelism has become a necessity for training modern large-scale deep language models. In this work, we identify a new and orthogonal dimension from existing model parallel…
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Zhuohan Li, Eric Wallace, Sheng Shen +4
Since hardware resources are limited, the objective of training deep learning models is typically to maximize accuracy subject to the time and memory constraints of training and in…
Fast Structured Decoding for Sequence Models
Zhiqing Sun, Zhuohan Li, Haoqing Wang +3
Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffe…
Hint-Based Training for Non-Autoregressive Machine Translation
Zhuohan Li, Zi Lin, Di He +4
Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer…
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
Yiping Lu, Zhuohan Li, Di He +5
The Transformer architecture is widely used in natural language processing. Despite its success, the design principle of the Transformer remains elusive. In this paper, we provide…
Towards Binary-Valued Gates for Robust LSTM Training
Zhuohan Li, Di He, Fei Tian +4
Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some…