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Ruihao Gong

27 papers hereh-index 254.3k citations40 works total

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

author position
  • first author1
  • middle author20
  • last author1

Across the 22 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CV12
  • cs.LG9
  • cs.CL3
  • cs.NE1
  • cs.PF1
  • cs.SE1
same name
  • Ruihao Gong — 21 papers, h 9
  • Ruihao Gong — 7 papers, h 3
  • Ruihao Gong — 1 paper, 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
20192026
most citedRobustART: Benchmarking Robustness on Architecture Design and Training Techniques

51 citations · 219 across the 18 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing

Yuhang Li, Feng Zhu, Ruihao Gong +5

User data confidentiality protection is becoming a rising challenge in the present deep learning research. Without access to data, conventional data-driven model compression faces…

cs.CV2020

Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search

Mingzhu Shen, Feng Liang, Ruihao Gong +6

Quantization Neural Networks (QNN) have attracted a lot of attention due to their high efficiency. To enhance the quantization accuracy, prior works mainly focus on designing advan…

cs.NE2020

Binary Neural Networks: A Survey

Haotong Qin, Ruihao Gong, Xianglong Liu +3

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarizat…

cs.LG2020

Efficient Bitwidth Search for Practical Mixed Precision Neural Network

Yuhang Li, Wei Wang, Haoli Bai +3

Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activati…

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