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Yelong Shen

51 papers hereh-index 4028.3k citations82 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author45

Across the 50 of 51 papers where every author was matched, so the position is known.

fields
  • cs.CL38
  • cs.CV4
  • cs.LG4
  • cs.AI2
  • cs.IR2
  • cs.SE1
same name
  • Yelong Shen — 23 papers, h 15
  • Yelong Shen — 14 papers, h 8
  • Yelong Shen — 10 papers, h 6
  • Yelong Shen — 4 papers, h 2
  • Yelong Shen — 4 papers, h 5
  • Yelong Shen — 3 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142024
most citedLoRA: Low-Rank Adaptation of Large Language Models

2.5k citations · 4.3k across the 50 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021★ 2 cited

Memory-Efficient Differentiable Transformer Architecture Search

Yuekai Zhao, Li Dong, Yelong Shen +3

Differentiable architecture search (DARTS) is successfully applied in many vision tasks. However, directly using DARTS for Transformers is memory-intensive, which renders the searc…

cs.LG2016★ 287 cited

ReasoNet: Learning to Stop Reading in Machine Comprehension

Yelong Shen, Po-Sen Huang, Jianfeng Gao +1

Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architect…

cs.LG2015★ 21 cited

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

Jianshu Chen, Ji He, Yelong Shen +5

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i.e., BP-sLDA), which maximizes the posterior pro…

cs.LG2015

A Deep Embedding Model for Co-occurrence Learning

Yelong Shen, Ruoming Jin, Jianshu Chen +3

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and product…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.