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..
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Authors: Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, Yonghong Tian
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that the proposed method achieves minimal loss of SNNs performance on MNIST and CIFAR-10 datasets so far. |
| Researcher Affiliation | Academia | Yanqi Chen1,3 , Zhaofei Yu1,2,3 , Wei Fang1,3 , Tiejun Huang1,2,3 and Yonghong Tian1,3 1Department of Computer Science and Technology, Peking University 2Institute for Artificial Intelligence, Peking University 3Peng Cheng Laboratory |
| Pseudocode | Yes | Algorithm 1: Gradient Rewiring with SGD |
| Open Source Code | Yes | Our codes are available at https://github. com/Yanqi-Chen/Gradient-Rewiring. |
| Open Datasets | Yes | We evaluate the performance of the Grad R algorithm on image recognition benchmarks, namely the MNIST and CIFAR10 datasets. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly provide details about a separate validation set split or its size/percentage. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Our implementation of deep SNN are based on our open-source SNN framework Spiking Jelly [Fang et al., 2020a]' and the use of 'Adam optimizer' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The choice of all hyperparameters is shown in Table 3 and Table 4. Table 3 lists 'N # Epoch 2048', 'Batch Size 16', 'T # Timestep 8', 'τm Membrane Constant 2.0', 'uth Firing Threshold 1.0', 'urest Resting Potential 0.0', 'p Target Sparsity 95%', 'η Learning Rate 1e-4', 'β1, β2 Adam Decay 0.9, 0.999' for CIFAR-10, and different values for MNIST. |