Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 17,535 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,369 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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.'. |