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Xiaodan Song

11 papers hereh-index 218.2k citations39 works total

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

author position
  • first author1
  • middle author7
  • last author3

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG3
  • cs.CL1
  • cs.MM1
  • cs.NI1
  • eess.IV1
same name
  • Xiaodan Song — 2 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
20182021
most citedLarge Batch Optimization for Deep Learning: Training BERT in 76 minutes

162 citations · 423 across the 11 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 2 cited

Efficient Scale-Permuted Backbone with Learned Resource Distribution

Xianzhi Du, Tsung-Yi Lin, Pengchong Jin +4

Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combini…

cs.LG2020★ 5 cited

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

Denny Zhou, Mao Ye, Chen Chen +6

For deploying a deep learning model into production, it needs to be both accurate and compact to meet the latency and memory constraints. This usually results in a network that is…

cs.CL2020★ 94 cited

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

Zhiqing Sun, Hongkun Yu, Xiaodan Song +3

Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from he…

cs.CV2020★ 39 cited

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

Jiahui Yu, Pengchong Jin, Hanxiao Liu +7

Neural architecture search (NAS) has shown promising results discovering models that are both accurate and fast. For NAS, training a one-shot model has become a popular strategy to…

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