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..
Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
Authors: Christian Schmid, James M Murray
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we verify our approach with real data using the MNIST dataset. We characterize the effects of the learning rule (supervised or reinforcement learning, SL/RL) and input-data distribution on the perceptron s learning curve and the forgetting curve as subsequent tasks are learned. |
| Researcher Affiliation | Academia | Christian Schmid Institute of Neuroscience University of Oregon EMAIL James M. Murray Institute of Neuroscience University of Oregon EMAIL |
| Pseudocode | No | The paper provides mathematical derivations and equations but does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | All the source code files can be found in the supplemental materials, which should allow a reader to straightforwardly recreate all experimental results. |
| Open Datasets | Yes | Additionally, we verify our approach with real data using the MNIST dataset. |
| Dataset Splits | No | The paper refers to training and testing on datasets, including a 'hold-out set' for testing, but does not explicitly specify train/validation/test splits with percentages or counts. |
| Hardware Specification | Yes | The computations were performed on an NVIDIA Titan Xp GPU, with runtimes of at most a few minutes. |
| Software Dependencies | Yes | The numerical code implementing the model and performing the analyses was mostly written in JAX [Bradbury et al., 2018], as well as Wolfram Mathematica and Sci Py [Virtanen et al., 2020]. (The citation for Sci Py specifically mentions 'Sci Py 1.0'). |
| Experiment Setup | Yes | For Fig. 2, the flow fields were plotted for the limit of zero input noise and a regularization parameter of λ = 0.1. The learning curves are plotted for λ = 0 and Σ = σ2I, with σ = 0.1 and σ = 1... We set λ = 1. For the forgetting curves in Fig. 6, we set λ = 10 and the learning rate to η = 10^-2... For all other simulations, the learning rate was set to η = 10^-3. |