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..
(Non-)Convergence Results for Predictive Coding Networks
Authors: Simon Frieder, Thomas Lukasiewicz
ICML 2022 | Venue PDF | 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. |