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..
A Differentiable Point Process with Its Application to Spiking Neural Networks
Authors: Hiroshi Kajino
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the effectiveness of our gradient estimator through numerical simulation. |
| Researcher Affiliation | Industry | IBM Research Tokyo, Tokyo, Japan. Correspondence to: Hiroshi Kajino <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Thinning algorithm for MPP Algorithm 2 Thinning algorithm for PP Algorithm 3 Generic learning algorithm |
| Open Source Code | Yes | All the experiments are conducted on IBM Cloud4, and the code is publicly available (Kajino, 2021). ... Kajino, H. diffsnn, 2021. URL https://github.com/ ibm-research-tokyo/diffsnn. |
| Open Datasets | No | Data set. We use a synthetic data set generated by the vanilla SNN (Equation (7)). ... We generate training/test sets consisting of Ntrain/100 examples of length 50 respectively. |
| Dataset Splits | Yes | We generate training/test sets consisting of Ntrain/100 examples of length 50 respectively. |
| Hardware Specification | Yes | All the experiments are conducted on IBM Cloud4, and the code is publicly available (Kajino, 2021). [Footnote 4]: Intel Xeon Gold 6248 2.50GHz 48 cores and 192GB memory. |
| Software Dependencies | No | The paper mentions using "Ada Grad (Duchi et al., 2011)" as an optimizer but does not specify version numbers for any key software components or libraries (e.g., Python, PyTorch, etc.). |
| Experiment Setup | Yes | Network size D = 6, |O| = 2, |H| = 4 Activation/ο¬lter functions a = 5, L = 2, s1 = 0, s2 = 10 PP Ο = 0.3, Ξ» = 20 # of samplings 100 (Eq. (5)), 1 (Eq. (9)). ... with initial learning rate 0.05 for 10 epochs. |