most citedTorchScale: Transformers at Scale

6 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20226 cited

TorchScale: Transformers at Scale

Shuming Ma, Hongyu Wang, Shaohan Huang +8

Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with be…

cs.CV20222 cited

Linear Video Transformer with Feature Fixation

Kaiyue Lu, Zexiang Liu, Jianyuan Wang +8

Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some stud…

cs.CL20224 cited

CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation

Jian Yang, Shaohan Huang, Shuming Ma +6

Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. Cross-lingual NER has been proposed…

cs.AI2022

On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies

Haozhi Wang, Qing Wang, Yunfeng Shao +3

Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize polic…

cs.CL20223 cited

Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals

Meng Xiao, Ziyue Qiao, Yanjie Fu +5

The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has bee…

math.NA2021

The global landscape of phase retrieval II: quotient intensity models

Jian-Feng Cai, Meng Huang, Dong Li +1

A fundamental problem in phase retrieval is to reconstruct an unknown signal from a set of magnitude-only measurements. In this work we introduce three novel quotient intensity-bas…