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Weifeng Zhang

4 papers hereh-index 9263 citations15 works total

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

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.DC1
  • cs.NE1
  • cs.SE1
  • stat.ML1
same name
  • Weifeng Zhang — 3 papers, h 6
  • Weifeng Zhang — 2 papers
  • Weifeng Zhang — 2 papers
  • Weifeng Zhang — 1 paper, h 3
  • Weifeng Zhang — 1 paper

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

most citedHardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM

8 citations · 13 across the 3 of their papers we have counts for

collaborators

4 papers

stat.ML2020

Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness

Huijie Feng, Chunpeng Wu, Guoyang Chen +2

Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic…

cs.SE2019★ 5 cited

Sionnx: Automatic Unit Test Generator for ONNX Conformance

Xinli Cai, Peng Zhou, Shuhan Ding +2

Open Neural Network Exchange (ONNX) is an open format to represent AI models and is supported by many machine learning frameworks. While ONNX defines unified and portable computati…

cs.DC2019

Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture

Ye Yu, Yingmin Li, Shuai Che +2

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, r…

cs.NE2019★ 8 cited

Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM

Hongxu Yin, Guoyang Chen, Yingmin Li +3

Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promisin…

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