Defending neural backdoors via generative distribution modeling X Qiao*, Y Yang*, H Li Advances in Neural Information Processing Systems (NeurIPS), 2019 | 159 | 2019 |
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks Y Yang, W Zhang, P Li Proceedings of the 38th International Conference on Machine Learning (ICML), 2021 | 21 | 2021 |
Msnet: Structural wired neural architecture search for internet of things HP Cheng, T Zhang, Y Yang, F Yan, H Teague, Y Chen, H Li Proceedings of the International Conference on Computer Vision (ICCV …, 2019 | 18 | 2019 |
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures HP Cheng, T Zhang, Y Yang, F Yan, S Li, H Teague, H Li, Y Chen arXiv preprint arXiv:1906.08305, 2019 | 12 | 2019 |
Local prediction-learning in high-dimensional spaces enables neural networks to plan C Stöckl, Y Yang, W Maass Nature Communications 15 (1), 2344, 2024 | 3 | 2024 |
BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks Y Yang, P Li arXiv preprint arXiv:2111.13188, 2021 | 3 | 2021 |
A theory for the sparsity emerged in the Forward Forward algorithm Y Yang arXiv preprint arXiv:2311.05667, 2023 | 1 | 2023 |
Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry Y Yang, P Li arXiv preprint arXiv:2212.09440, 2022 | 1 | 2022 |
Temporal surrogate back-propagation for spiking neural networks Y Yang arXiv preprint arXiv:2011.09964, 2020 | 1 | 2020 |
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation Y Yang, P Li arXiv preprint arXiv:2205.07292, 2022 | | 2022 |