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researcher

Zhen Dong

UC Berkeley

31 papers hereh-index 225.5k citations46 works total

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

author position
  • first author5
  • middle author23
  • last author1

Across the 29 of 31 papers where every author was matched, so the position is known.

fields
  • cs.CV18
  • cs.CL7
  • cs.LG4
  • cs.AR1
  • eess.IV1
affiliations
  • UC Berkeley
Homepage
same name
  • Zhen Dong — 15 papers, h 5
  • Zhen Dong — 11 papers, h 8
  • Zhen Dong — 9 papers, h 4
  • Zhen Dong — 6 papers, h 4
  • Zhen Dong — 6 papers, h 4
  • Zhen Dong — 6 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
20192024
most citedApplications and Techniques for Fast Machine Learning in Science

77 citations · 368 across the 31 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021★ 77 cited

Applications and Techniques for Fast Machine Learning in Science

Allison McCarn Deiana, Nhan Tran, Joshua Agar +84

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…

cs.CV2021★ 2 cited

HAO: Hardware-aware neural Architecture Optimization for Efficient Inference

Zhen Dong, Yizhao Gao, Qijing Huang +3

Automatic algorithm-hardware co-design for DNN has shown great success in improving the performance of DNNs on FPGAs. However, this process remains challenging due to the intractab…

cs.CV2021★ 42 cited

A Survey of Quantization Methods for Efficient Neural Network Inference

Amir Gholami, Sehoon Kim, Zhen Dong +3

As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the nume…

cs.CV2021★ 5 cited

Hessian-Aware Pruning and Optimal Neural Implant

Shixing Yu, Zhewei Yao, Amir Gholami +4

Pruning is an effective method to reduce the memory footprint and FLOPs associated with neural network models. However, existing structured-pruning methods often result in signific…

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