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researcher

Yu Liang

7 papers hereh-index 5108 citations9 works total

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

author position
  • first author2
  • middle author5

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

fields
  • cs.CV6
  • cs.LG1
same name
  • Yu Liang — 11 papers, h 4
  • Yu Liang — 9 papers, h 4
  • Yu Liang — 7 papers, h 5
  • Yu Liang — 6 papers, h 4
  • Yu Liang — 5 papers, h 3
  • Yu Liang — 4 papers, h 7

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
20242026
most citedQP-SNN: Quantized and Pruned Spiking Neural Networks

2 citations · 4 across the 7 of their papers we have counts for

collaborators
Showing 2025 · cs.CVShow all

4 papers · 2 filters

cs.CV2025

Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism

Yu Liang, Wenjie Wei, Ammar Belatreche +5

Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantage…

cs.CV2025★ 1 cited

Spiking Vision Transformer with Saccadic Attention

Shuai Wang, Malu Zhang, Dehao Zhang +7

The combination of Spiking Neural Networks (SNNs) and Vision Transformers (ViTs) holds potential for achieving both energy efficiency and high performance, particularly suitable fo…

cs.CV2025★ 2 cited

QP-SNN: Quantized and Pruned Spiking Neural Networks

Wenjie Wei, Malu Zhang, Zijian Zhou +6

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient pa…

cs.CV2025★ 1 cited

Binary Event-Driven Spiking Transformer

Honglin Cao, Zijian Zhou, Wenjie Wei +6

Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficienc…

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