431 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Peking UniversityCN31 papers
- Tsinghua UniversityCN11 papers
- Beijing Academy of Artificial IntelligenceCN5 papers
- Beijing National Laboratory for Molecular SciencesCN5 papers
- University of Chinese Academy of SciencesCN5 papers
- Chinese Academy of SciencesCN4 papers
- Chinese Institute for Brain ResearchCN4 papers
- Institute of PhysicsPL4 papers
- Songshan Lake Materials LaboratoryCN4 papers
- Collaborative Innovation Center of Quantum MatterCN3 papers
- Dana-Farber Cancer InstituteUS3 papers
- Harvard UniversityUS3 papers
5 papers · 1 filter
Bio-realistic and versatile artificial dendrites made of anti-ambipolar transistors
Yifei Yang, Mingkun Xu, Jing Pei +4
The understanding of neural networks as neuron-synapse binaries has been the foundation of neuroscience, and therefore, the emerging neuromorphic computing technology that takes in…
Space-variant Shack-Hartmann wavefront sensing based on affine transformation estimation
Fan Feng, Chen Liang, Dongdong Chen +7
The space-variant wavefront reconstruction problem inherently exists in deep tissue imaging. In this paper,we propose a framework of Shack-Hartmann wavefront space-variant sensing…
Observation of Gapped Dirac Cones in a Two-Dimensional Su-Schrieffer-Heeger Lattice
Daiyu Geng, Hui Zhou, Shaosheng Yue +6
The Su-Schrieffer-Heeger (SSH) model in a two-dimensional rectangular lattice features gapless or gapped Dirac cones with topological edge states along specific peripheries. While…
Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions
Ning Yang, Chao Tang, Yuhai Tu
Generalization is one of the most important problems in deep learning (DL). In the overparameterized regime in neural networks, there exist many low-loss solutions that fit the tra…
Learning Robust Representation through Graph Adversarial Contrastive Learning
Jiayan Guo, Shangyang Li, Yue Zhao +1
Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matr…