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Lei Cheng

8 papers hereh-index 4212 citations9 works total

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

author position
  • first author1
  • middle author6

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL2
  • cs.CV1
  • cs.GR1
same name
  • Lei Cheng — 8 papers, h 2
  • Lei Cheng — 7 papers, h 13
  • Lei Cheng — 6 papers, h 9
  • Lei Cheng — 6 papers, h 5
  • Lei Cheng — 6 papers, h 4
  • Lei Cheng — 4 papers, h 9

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
20232025
most citedLayerwise Change of Knowledge in Neural Networks

1 citations · 2 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

Technical Report: Quantifying and Analyzing the Generalization Power of a DNN

Yuxuan He, Junpeng Zhang, Lei Cheng +2

This paper proposes a new perspective for analyzing the generalization power of deep neural networks (DNNs), i.e., directly disentangling and analyzing the dynamics of generalizabl…

cs.LG2025

Revisiting Generalization Power of a DNN in Terms of Symbolic Interactions

Lei Cheng, Junpeng Zhang, Qihan Ren +1

This paper aims to analyze the generalization power of deep neural networks (DNNs) from the perspective of interactions. Unlike previous analysis of a DNN's generalization power in…

cs.LG2025

Randomness of Low-Layer Parameters Determines Confusing Samples in Terms of Interaction Representations of a DNN

Junpeng Zhang, Lei Cheng, Qing Li +2

In this paper, we find that the complexity of interactions encoded by a deep neural network (DNN) can explain its generalization power. We also discover that the confusing samples…

cs.LG2024★ 1 cited

Layerwise Change of Knowledge in Neural Networks

Xu Cheng, Lei Cheng, Zhaoran Peng +3

This paper aims to explain how a deep neural network (DNN) gradually extracts new knowledge and forgets noisy features through layers in forward propagation. Up to now, although th…

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