Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
Authors: Christian Schmid, James M Murray
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 cschmid9@uoregon.edu James M. Murray Institute of Neuroscience University of Oregon jmurray9@uoregon.edu |
| 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. |