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..
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Authors: Mohammad Reza Keshtkaran, Chethan Pandarinath
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When applied to data from motor cortex recorded while monkeys made reaches in various directions, large-scale HP optimization allowed SAEs to better maintain performance for small dataset sizes. |
| Researcher Affiliation | Academia | Mohammad Reza Keshtkaran Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech Atlanta, GA 30322 EMAIL; Chethan Pandarinath Coulter Dept. of Biomedical Engineering Dept of Neurosurgery Emory University and Georgia Tech Atlanta, GA 30322 EMAIL |
| Pseudocode | No | The paper describes the LFADS architecture and its components but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using a 'publicly-available LFADS codepack' but does not provide a link or explicit statement about making the code for their specific contributions (Sample Validation, Coordinated Dropout, HP optimization framework) publicly available. |
| Open Datasets | Yes | The second dataset we analyzed is publicly available (indy_20160426_01 [25]). [25] Joseph E. O Doherty, Mariana M. B. Cardoso, Joseph G. Makin, and Philip N. Sabes. Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology [Data set], May 2017. Zenodo. http://doi.org/10.5281/zenodo.583331. |
| Dataset Splits | Yes | In all cases, 80% of trials were used for model training, while 20% were held-out for validation. |
| Hardware Specification | No | The paper mentions running experiments on 'a local cluster' but does not provide specific details about the hardware specifications such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using a 'publicly-available LFADS codepack' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Table 1: List of HPs searched with PBT: L2 Gen scale (5, 5e4) log-uniform, L2 Con scale (5, 5e4) log-uniform, KL IC scale (0.05, 5) log-uniform, KL CO scale (0.05, 5) log-uniform, Dropout (0, 0.7) uniform, Keep ratio (0.3, 0.99) 0.5, Learning rate (10 5, 0.02) 0.01. |