(Non-)Convergence Results for Predictive Coding Networks
Authors: Simon Frieder, Thomas Lukasiewicz
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we use dynamical systems theory to formally investigate the convergence of PCNs as they are used in machine learning. Doing so, we put their theory on a firm, rigorous basis, by developing a precise mathematical framework for PCN and show that for sufficiently small weights and initializations, PCNs converge for any input. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford, UK. 2Institute of Logic and Computation, TU Wien, Austria. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical analysis and mathematical proofs, not empirical training on a dataset. Although it mentions 'a dataset consisting of a single training example' in the context of theoretical training stage analysis, it does not use a publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper describes theoretical analysis and mathematical proofs, and does not involve empirical experiments requiring dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper discusses mathematical parameters and conditions for convergence (e.g., 'sufficiently small weights and initializations', 'step size in γ (0, 1)') but these are part of the theoretical analysis, not a description of an empirical experimental setup with hyperparameters for a runnable system. |