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Chen Liang

Google Brain

20 papers hereh-index 234.9k citations93 works total

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

author position
  • first author6
  • middle author13

Across the 19 of 20 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • eess.SY5
  • cs.CL3
  • cs.IT1
  • math.OC1
affiliations
  • Google Brain
Homepage
same name
  • Chen Liang — 21 papers, h 15
  • Chen Liang — 12 papers, h 36
  • Chen Liang — 9 papers, h 2
  • Chen Liang — 7 papers, h 11
  • Chen Liang — 6 papers, h 4
  • Chen Liang — 6 papers, h 4

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
20162023
most citedThe Evolved Transformer

196 citations · 910 across the 20 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.IT2021

On Thermodynamic Interpretation of Copula Entropy

Jian Ma

Copula Entropy (CE) is a recently introduced concept for measuring correlation/dependence in information theory. In this paper, the theory of CE is introduced and the thermodynamic…

math.OC2021★ 5 cited

A Spectral Representation of Power Systems with Applications to Adaptive Grid Partitioning and Cascading Failure Localization

Alessandro Zocca, Chen Liang, Linqi Guo +2

Transmission line failures in power systems propagate and cascade non-locally. This well-known yet counter-intuitive feature makes it even more challenging to optimally and reliabl…

cs.LG2021★ 129 cited

Carbon Emissions and Large Neural Network Training

David Patterson, Joseph Gonzalez, Quoc Le +6

The computation demand for machine learning (ML) has grown rapidly recently, which comes with a number of costs. Estimating the energy cost helps measure its environmental impact a…

cs.LG2021★ 7 cited

PyGlove: Symbolic Programming for Automated Machine Learning

Daiyi Peng, Xuanyi Dong, Esteban Real +7

Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML soft…

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