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Beidi Chen

10 papers hereh-index 193.2k citations46 works total

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

author position
  • first author3
  • middle author5

Across the 8 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.DC1
  • cs.IR1
  • stat.AP1
same name
  • Beidi Chen — 8 papers
  • Beidi Chen — 7 papers, h 7
  • Beidi Chen — 7 papers, h 6
  • Beidi Chen — 3 papers, h 7
  • Beidi Chen — 3 papers, h 6
  • Beidi Chen — 2 papers

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
20172022
most citedScatterbrain: Unifying Sparse and Low-rank Attention Approximation

9 citations · 30 across the 7 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020

A Tale of Two Efficient and Informative Negative Sampling Distributions

Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger +3

Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full…

cs.LG2020★ 2 cited

SOLAR: Sparse Orthogonal Learned and Random Embeddings

Tharun Medini, Beidi Chen, Anshumali Shrivastava

Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the q…

cs.LG2020

Discovering Traveling Companions using Autoencoders

Xiaochang Li, Bei Chen, Xuesong Lu

With the wide adoption of mobile devices, today's location tracking systems such as satellites, cellular base stations and wireless access points are continuously producing tremend…

cs.IR2020★ 3 cited

Climbing the WOL: Training for Cheaper Inference

Zichang Liu, Zhaozhuo Xu, Alan Ji +3

Efficient inference for wide output layers (WOLs) is an essential yet challenging task in large scale machine learning. Most approaches reduce this problem to approximate maximum i…

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