Reverse Differentiation via Predictive Coding
Authors: Tommaso Salvatori, Yuhang Song, Zhenghua Xu, Thomas Lukasiewicz, Rafal Bogacz8150-8158
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally analyze the running time of Z-IL, IL, and BP on different architectures. The results of BP, IL, and Z-IL, averaged over all the experiments per model, are reported in Table 2, and a detailed description of the experiments, as well as all the parameters needed to reproduce the results, are provided in the supplementary material. |
| Researcher Affiliation | Academia | Tommaso Salvatori 1, Yuhang Song 1, 2 *, Zhenghua Xu 3, Thomas Lukasiewicz 1, Rafal Bogacz 2 1 Department of Computer Science, University of Oxford, UK 2 MRC Brain Network Dynamics Unit, University of Oxford, UK 3 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China |
| Pseudocode | Yes | Algorithm 1: Learning one training pair ( s, y) with Z-IL; Algorithm 3: Z-IL for computational graphs. |
| Open Source Code | No | The paper explicitly states that 'a detailed description of the experiments, as well as all the parameters needed to reproduce the results, are provided in the supplementary material.' However, it does not explicitly mention the release of source code for the methodology or provide a link to a repository. |
| Open Datasets | No | The paper discusses experiments on different neural network architectures (MLP, CNNs, RNNs, ResNet18, Transformer) and refers to training with 'data point s' or 'labelled point (s, y)'. However, it does not explicitly name any specific public datasets used, nor does it provide any concrete access information (links, DOIs, formal citations) for any dataset. |
| Dataset Splits | No | The paper does not provide specific details about dataset splits (e.g., percentages, sample counts for training, validation, or testing sets). It mentions training on 'a labelled point (s, y)' in Algorithm 1, but no broader dataset splitting strategy is described for the experiments. |
| Hardware Specification | No | The paper acknowledges 'the use of the EPSRC-funded Tier 2 facility JADE (EP/P020275/1) and GPU computing support by Scan Computers International Ltd.' However, it does not specify exact GPU models, CPU types, or other detailed hardware components used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, such as programming languages, libraries, or frameworks used for the experiments. |
| Experiment Setup | Yes | The paper states: 'a detailed description of the experiments, as well as all the parameters needed to reproduce the results, are provided in the supplementary material.' |