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Jiajun Shen

8 papers hereh-index 4171 citations8 works total

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

author position
  • middle author7
  • last author1

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL3
  • cs.AI1
same name
  • Jiajun Shen — 18 papers, h 16
  • Jiajun Shen — 4 papers, h 3
  • Jiajun Shen — 3 papers, h 2
  • Jiajun Shen — 2 papers, h 1
  • Jiajun Shen — 2 papers, h 3
  • Jiajun Shen — 2 papers, h 6

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
20232026
most citedDiLoCo: Distributed Low-Communication Training of Language Models

7 citations · 7 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Latent Space Communication via K-V Cache Alignment

Lucio M. Dery, Zohar Yahav, Henry Prior +3

Solving increasingly complex problems with large language models (LLMs) necessitates a move beyond individual models and towards multi-model systems that can effectively collaborat…

cs.LG2024

DiPaCo: Distributed Path Composition

Arthur Douillard, Qixuan Feng, Andrei A. Rusu +7

Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodat…

cs.LG2024

Asynchronous Local-SGD Training for Language Modeling

Bo Liu, Rachita Chhaparia, Arthur Douillard +5

Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is an approach to distributed optimization where each device performs more than one SGD upda…

cs.LG2023★ 7 cited

DiLoCo: Distributed Low-Communication Training of Language Models

Arthur Douillard, Qixuan Feng, Andrei A. Rusu +6

Large language models (LLM) have become a critical component in many applications of machine learning. However, standard approaches to training LLM require a large number of tightl…

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