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..
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Authors: Kristopher Jensen, Ta-Chu Kao, Jasmine Stone, Guillaume Hennequin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply b GPFA to continuous recordings spanning 30 minutes with over 14 million data points from primate motor and somatosensory cortices during a self-paced reaching task. We validate our method on synthetic and biological data, where b GPFA exhibits superior performance to GPFA and Poisson GPFA with increased scalability and without requiring cross-validation to select the latent dimensionality. We then apply b GPFA to longitudinal, multi-area recordings from primary motor (M1) and sensory (S1) areas during a monkey self-paced reaching task spanning 30 minutes. |
| Researcher Affiliation | Academia | Kristopher T. Jensen* Ta-Chu Kao* Jasmine T. Stone Guillaume Hennequin Department of Engineering University of Cambridge EMAIL |
| Pseudocode | Yes | The algorithm is described in pseudocode with further implementation and computational details in Appendix L. |
| Open Source Code | Yes | Here, we provide a ready-to-use Python package with GPU implementations of not only b GPFA with ARD, but also standard GPFA and Factor Analysis with Gaussian and non-Gaussian noise models. |
| Open Datasets | Yes | We applied b GPFA to biological data recorded from a rhesus macaque during a self-paced reaching task with continuous recordings spanning 30 minutes (37, 42; Figure 3a)." and "We are grateful to O Doherty et al. [42] for making their data publicly available and to Marine Schimel and David Liu for insightful discussions." (Reference 42: "O Doherty, J. E., Cardoso, M., Makin, J., and Sabes, P. (2017). Nonhuman primate reaching with multichannel sensorimotor cortex electrophysiology. Zenodo http://doi. org/10.5281/zenodo, 583331.") |
| Dataset Splits | No | The paper discusses 'cross-validation' and 'training data' but does not specify explicit percentages, sample counts, or refer to a named standard split for training, validation, and test datasets. |
| Hardware Specification | No | The paper does not specify the exact hardware components (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions a 'Python package with GPU implementations' but does not provide specific version numbers for Python, any deep learning frameworks (e.g., PyTorch, TensorFlow), or CUDA libraries. |
| Experiment Setup | No | The paper describes the optimization process using stochastic gradient ascent with Adam and mini-batches but does not provide specific hyperparameter values such as learning rate, batch size, or number of epochs. |