Zhaofei Yu
助理教授
Researches brain-inspired computing and spiking neural networks.
Zhaofei Yu's lab at Peking works on computational neuroscience & cognition. 83 papers since 2023, 59 at top venues, mostly CVPR and ICLR. Applications close .
At a glance
- papers, 2023–2026
- 22/ year
- at T1 venues
- 71%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 12 papers2026 · T1 9 · collecting
- 22 papers2025 · T1 14
- 32 papers2024 · T1 23
- 17 papers2023 · T1 13
T1 venuesOtherVenues: CVPR, ICLR, AAAI, NeurIPS, ICML
Research focuspaper titles that use each topic's words
- spiking29 papers
- spiking neural19 papers
- brain-inspired1 paper
- Computational neuroscience & cognition0 papers
- brain-inspired computing0 papers
Paper list
From DBLP.
202612 papers
- ICLR (T1)CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning (opens in a new tab)
- IEEE Trans. Intell. Transp. Syst.Cooperative Perception of Multi-Agents Under the Spatio-Temporal Drift Issue (opens in a new tab)
- AAAI (T1)Generalized Threshold Optimization with Harmony Multi-Threshold Neurons for Accurate ANN-to-SNN Conversion (opens in a new tab)
- IEEE Trans. Artif. Intell.Inner-Probe: Discovering Copyright-Related Data Generation in LLM Architecture (opens in a new tab)
- ICLR (T1)PredNext: Explicit Cross-View Temporal Prediction for Unsupervised Learning in Spiking Neural Networks (opens in a new tab)
- CVPR (T1)Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model (opens in a new tab)
- TPAMI (T1)SNNTracker: Online High-Speed Multi-Object Tracking With Spike Camera (opens in a new tab)
- AAAI (T1)Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Streams (opens in a new tab)
- Sci. China Inf. Sci.SpikeCV: open a continuous computer vision era (opens in a new tab)
- ICLR (T1)SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams (opens in a new tab)
- CVPR (T1)Spk2VidNet: A Hierarchical Recurrent Architecture for High-Fidelity Video Reconstruction from Long Spike-Camera Streams (opens in a new tab)
- ICLR (T1)Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition (opens in a new tab)
202522 papers
- ICML (T1)A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing (opens in a new tab)
- NeurIPS (T1)Activity Pruning for Efficient Spiking Neural Networks (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Assisting Training of Deep Spiking Neural Networks With Parameter Initialization (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Converting High-Performance and Low-Latency SNNs Through Explicit Modeling of Residual Error in ANNs (opens in a new tab)
- Neural NetworksCorrigendum to "Implementing feature binding through dendritic networks of a single neuron" [Neural Networks(2025) 107555] (opens in a new tab)
- Neural NetworksDecoding natural visual scenes via learnable representations of neural spiking sequences (opens in a new tab)
- ICML (T1)Differential Coding for Training-Free ANN-to-SNN Conversion (opens in a new tab)
- IEEE Trans. Circuits Syst. Video Technol.Enhancing NR-IQA Model Robustness Through Simple Image Compression Techniques (opens in a new tab)
- ICML (T1)Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation (opens in a new tab)
- Neural NetworksImplementing feature binding through dendritic networks of a single neuron (opens in a new tab)
- CVPR (T1)Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications (opens in a new tab)
- NeurIPS (T1)Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control (opens in a new tab)
202432 papers
- ICLR (T1)A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model (opens in a new tab)
- ICML (T1)Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks (opens in a new tab)
- CVPR (T1)Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations (opens in a new tab)
- NeurIPS (T1)Continuous Spatiotemporal Events Decoupling through Spike-based Bayesian Computation (opens in a new tab)
- IEEE Trans. Cogn. Dev. Syst.Converting Artificial Neural Networks to Ultralow-Latency Spiking Neural Networks for Action Recognition (opens in a new tab)
- PLoS Comput. Biol.Decoding dynamic visual scenes across the brain hierarchy (opens in a new tab)
- ICML (T1)Enhancing Adversarial Robustness in SNNs with Sparse Gradients (opens in a new tab)
- AAAI (T1)Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms (opens in a new tab)
- CVPR (T1)Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios (opens in a new tab)
- CVPR (T1)Intensity-Robust Autofocus for Spike Camera (opens in a new tab)
- NeurIPS (T1)LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model (opens in a new tab)
- IEEE Trans. Mob. Comput.Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing (opens in a new tab)
202317 papers
- ICLR (T1)A Unified Framework for Soft Threshold Pruning (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Asynchronous Spatiotemporal Spike Metric for Event Cameras (opens in a new tab)
- ICLR (T1)Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes (opens in a new tab)
- TPAMI (T1)Capture the Moment: High-Speed Imaging With Spiking Cameras Through Short-Term Plasticity (opens in a new tab)
- CVPR (T1)Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting (opens in a new tab)
- IEEE Trans. Cybern.Deep Spike Learning With Local Classifiers (opens in a new tab)
- PLoS Comput. Biol.Diverse role of NMDA receptors for dendritic integration of neural dynamics (opens in a new tab)
- NeurIPS (T1)Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams (opens in a new tab)
- NeurIPS (T1)Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks (opens in a new tab)
- AAAI (T1)Learning Temporal-Ordered Representation for Spike Streams Based on Discrete Wavelet Transforms (opens in a new tab)
- NeurIPS (T1)Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies (opens in a new tab)
- CVPR (T1)Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to PekingFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USUSC
Maja Mataric
- Human-robot interaction & assistive robots
- USCMU
Bryan Wilder
- Learning theory & optimization
- USUC Berkeley
Bin Yu
- Learning theory & optimization
- GBOxford
Markus Brill
- Reinforcement learning
- USUCSD
Rose Yu
38 T1 - USMichigan
Ambuj Tewari
- Learning theory & optimization
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- DBLP
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
Pekingcloses