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..
Semantically-correlated memories in a dense associative model
Authors: Thomas F Burns
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental demonstrations showcase CDAM s efficacy in handling realworld data, replicating a classical neuroscience experiment, performing image retrieval, and simulating arbitrary finite automata. |
| Researcher Affiliation | Academia | 1Institute for Computational and Experimental Research in Mathematics, Brown University, USA 2Sci AI Center, Cornell University, USA 3Neural Coding and Brain Computing Unit, OIST Graduate University, Japan. |
| Pseudocode | No | The paper does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | A copy of the code used is available at https://github.com/tfburns/CDAM. |
| Open Datasets | Yes | Here I use a directed cycle graph # C50 where the patterns are sparsely sampled frames of videos (see Appendix A.11 for details). The two videos used were sourced from Wikimedia Commons and were uploaded by User:Raul654 on 24 January 2006. They can found at the below URLs: https://commons.wikimedia.org/wiki/File: Gorilla_gorilla_gorilla1.ogv https://commons.wikimedia.org/wiki/File: Gorilla_gorilla_gorilla4.ogv... I test CDAM on the Fashion MNIST dataset (Xiao et al., 2017) |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. The experiments focus on demonstrating model dynamics and recall, not typical supervised learning evaluation with predefined splits. |
| Hardware Specification | Yes | All numerical simulations were performed on a Lenovo x260 Think Pad laptop computer using the Python 3 programming language. |
| Software Dependencies | No | The paper mentions 'Python 3 programming language' but does not specify version numbers for any key libraries, frameworks, or solvers used. |
| Experiment Setup | Yes | Unless otherwise stated, in the following numerical analyses I used n = 1, 000, β = 1, and η = 0.1. ...To initialise the network state, I chose a memory pattern µ and set σ(0) = ξµ + cζ, where ζ is a random vector with elements independently drawn from the interval [ 0.5, 0.5] and c R+ is the amplitude of the additive random noise, here c = 1. |