10 citations · 18 across the 6 of their papers we have counts for
7 papers
SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
Yimu Zhang, Dongqi Han, Yansen Wang +3
Spike sorting is an essential process in neural recording, which identifies and separates electrical signals from individual neurons recorded by electrodes in the brain, enabling r…
Toward Relative Positional Encoding in Spiking Transformers
Changze Lv, Yansen Wang, Dongqi Han +4
Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to…
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
Changze Lv, Dongqi Han, Yansen Wang +3
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying…
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Changze Lv, Yansen Wang, Dongqi Han +3
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying S…
Toward Open-ended Embodied Tasks Solving
William Wei Wang, Dongqi Han, Xufang Luo +4
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years. A major challenge is task open-endedness. In practi…
AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
Jie Lian, Xufang Luo, Caihua Shan +3
Precision medicine tailored to individual patients has gained significant attention in recent times. Machine learning techniques are now employed to process personalized data from…
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…