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researcher

Hao Li

95 papers hereh-index 8945.5k citations1.8k works total

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

author position
  • sole author3
  • first author16
  • middle author54
  • last author17

Across the 90 of 95 papers where every author was matched, so the position is known.

fields
  • cs.CV39
  • cs.LG10
  • quant-ph7
  • cond-mat.mes-hall5
  • cs.AI3
  • cs.CE3
same name
  • Hao Li — 96 papers, h 61
  • Hao Li — 50 papers, h 37
  • Hao Li — 30 papers, h 10
  • Hao Li — 25 papers, h 10
  • Hao Li — 20 papers, h 10
  • Hao Li — 19 papers, h 14

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
20142023
most citedPruning Filters for Efficient ConvNets

682 citations · 2.5k across the 94 of their papers we have counts for

collaborators
Showing 2021 · cs.LGShow all

4 papers · 2 filters

cs.LG2021★ 52 cited

Dash: Semi-Supervised Learning with Dynamic Thresholding

Yi Xu, Lei Shang, Jinxing Ye +5

While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…

cs.LG2021★ 2 cited

Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection

Cangning Fan, Fangyi Zhang, Peng Liu +5

Previous transfer methods for anomaly detection generally assume the availability of labeled data in source or target domains. However, such an assumption is not valid in most real…

cs.LG2021

Why Does Multi-Epoch Training Help?

Yi Xu, Qi Qian, Hao Li +1

Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in e…

cs.LG2021

A Theoretical Analysis of Learning with Noisily Labeled Data

Yi Xu, Qi Qian, Hao Li +1

Noisy labels are very common in deep supervised learning. Although many studies tend to improve the robustness of deep training for noisy labels, rare works focus on theoretically…

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