GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Authors: Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a series of simple computer vision experiments, we show near-identical performance between backpropagation and GAIT-prop with a soft orthogonality-inducing regularizer. |
| Researcher Affiliation | Academia | Nasir Ahmad Marcel van Gerven Luca Ambrogioni Department of Artificial Intelligence Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, the Netherlands {n.ahmad,m.vangerven,l.ambrogioni}@donders.ru.nl |
| Pseudocode | Yes | Algorithm 1 GAIT-prop (per training sample update) |
| Open Source Code | Yes | Code used to produce the results shown in this paper is available at https://github.com/nasiryahm/GAIT-prop. |
| Open Datasets | Yes | We make use of three image classification datasets: MNIST, Fashion-MNIST, and KMNIST. |
| Dataset Splits | No | The paper mentions training and testing, and a grid search for parameters, but does not explicitly detail the training/validation/test splits, such as percentages or sample counts for each split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimiser' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | In order to identify acceptable parameters for each of our learning methods, we ran a grid search for the learning rate η and the orthogonal regularizer strength λ. The highest-performing networks were tested for stability and stable high performing parameters were used. Details of specific parameters used and the grid search outcomes are provided in the Supplementary Material. |