Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Spiking Neural Network as Adaptive Event Stream Slicer
Authors: Jiahang Cao, Mingyuan Sun, Ziqing Wang, Hao Cheng, Qiang Zhang, shibo zhou, Renjing Xu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method yields significant performance improvements in event-based object tracking and recognition. |
| Researcher Affiliation | Collaboration | Jiahang Cao1 Mingyuan Sun2 Ziqing Wang3 Hao Cheng1 Qiang Zhang1,4 Shibo Zhou5 Renjing Xu1 1 The Hong Kong University of Science and Technology (Guangzhou) 2 Northeastern University 3 Northwestern University 4 Beijing Innovation Center of Humanoid Robotics Co. Ltd. 5 Brain Mind Innovation |
| Pseudocode | Yes | Algorithm 1 Adaptive Event Slicing Process and Algorithm 2 Feedback-Update Training Strategy |
| Open Source Code | Yes | Our code is available at https://github.com/Andy Cao1125/Spike Slicer. |
| Open Datasets | Yes | Datasets. The FE108 dataset [3] is an extensive event-based dataset for single object tracking... The DVS-Gesture [34] dataset contains 11 hand gestures... The N-Caltech101 dataset [35] incorporates 8,831 event-based images... The DVS-CIFAR10 dataset [36] is an event-stream dataset... The SL-Animal database [37] features DVS recordings... |
| Dataset Splits | No | We choose 54 sequences for training ANNs, 22 sequences for training SNNs and the rest 32 sequences for testing. |
| Hardware Specification | Yes | Each experiment is conducted in an NVIDIA 4090 GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., PyTorch, Python versions) are mentioned. |
| Experiment Setup | Yes | We adopt the SGD optimizer and set the initial learning rate as 1e-4, along with the cosine learning rate scheduler. SNN models are trained with batch size 32. |