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..
Enhancing Training of Spiking Neural Network with Stochastic Latency
Authors: Srinivas Anumasa, Bhaskar Mukhoty, Velibor Bojkovic, Giulia De Masi, Huan Xiong, Bin Gu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide heuristics for our approach with partial theoretical justification and experimental evidence showing the state-of-the-art performance of our models on datasets such as CIFAR-10, DVS-CIFAR10, CIFAR-100, and DVS-Gesture. Our code is available at https://github.com/srinuvaasu/SLT |
| Researcher Affiliation | Academia | 1 Mohamed bin Zayed University of Artificial Intelligence, UAE 2 ARRC, Technology Innovation Institute, UAE 3 Bio Robotics Institute, Sant Anna School of Advanced Studies Pisa, Italy 4 Harbin Institute of Technology, China 5 School of Artificial Intelligence, Jilin University, China |
| Pseudocode | Yes | Algorithm 1: SLT: Stochastic Latency Training |
| Open Source Code | Yes | Our code is available at https://github.com/srinuvaasu/SLT |
| Open Datasets | Yes | Our models on datasets such as CIFAR-10, DVS-CIFAR10, CIFAR-100, and DVS-Gesture. ... CIFAR-10 (Krizhevsky, Hinton et al. 2009) ... DVS-CIFAR-10 (Li et al. 2017) ... DVS-Gesture (Amir et al. 2017) |
| Dataset Splits | No | The paper mentions training and test sets (e.g., '5000 train images and 1000 test images' for CIFAR-10), but does not explicitly provide details for a validation split. |
| Hardware Specification | No | The paper mentions the use of 'GPUs' for training but does not provide specific hardware models, processors, or detailed specifications. |
| Software Dependencies | No | The paper specifies optimizer details like 'Adam' and 'Cosine Ann.' but does not list specific versions of software libraries or programming languages required for replication (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | Table 1: Hyper-parameter settings for comparison lists specific values for 'No. of epochs', 'Mini batch size', 'LIF: β', 'LIF: u0', 'LIF: uth', 'λT ET', 'Optimiser Adam', 'Learning Rate', 'Adam: Betas', 'Rate Scheduler Cosine Ann.'. |