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..
Direct Training of SNN using Local Zeroth Order Method
Authors: Bhaskar Mukhoty, Velibor Bojkovic, William de Vazelhes, Xiaohan Zhao, Giulia De Masi, Huan Xiong, Bin Gu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experimental validation of the technique on standard static datasets (CIFAR-10, CIFAR-100, Image Net100) and neuromorphic datasets (DVS-CIFAR-10, DVS-Gesture, N-Caltech-101, NCARS) and obtain results that offer improvement over the state-of-the-art results. |
| Researcher Affiliation | Collaboration | 1 Mohamed bin Zayed University of Artificial Intelligence, UAE 2 ARRC, Technology Innovation Institute, UAE 3 Nanjing University of Information Science and Technology, China 4 School of Artificial Intelligence, Jilin University, China 5 Harbin Institute of Technology, China 6 Bio Robotics Institute, Sant Anna School of Advanced Studies, Pisa, Italy |
| Pseudocode | Yes | Algorithm 1 LOCALZO |
| Open Source Code | Yes | The code is available at https://github.com/Bhaskar Mukhoty/Local ZO. |
| Open Datasets | Yes | standard static image datasets such as CIFAR-10, CIFAR-100[22], Image Net-100[12] and neuromorphic datasets such as DVS-CIFAR-10[24], DVS-Gesture[2], N-Caltech-101[30], N-CARS[37]. |
| Dataset Splits | No | The paper provides train and test image counts for datasets like CIFAR-10, CIFAR-100, and ImageNet-100 (e.g., 'each class respectively have (5000, 1000) train and test images' for CIFAR-10), but does not explicitly detail the size or methodology for a validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, or memory specifications) used to run its experiments. |
| Software Dependencies | No | The paper mentions 'Optimizer: Adam' but does not specify version numbers for any programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | Table 7 provides 'Hyper-parameter settings for general comparison' including 'Number epochs', 'Mini batch size', 'Learning Rate', 'Optimizer: Adam with betas: (0.9; 0.999)', and specific parameters for LIF and LOCALZO (δ, m, λ). |