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..
Predictive Attractor Models
Authors: Ramy Mounir, Sudeep Sarkar
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, University of South Florida, Tampa EMAIL |
| Pseudocode | Yes | Algorithm 1 : Sequence Learning. ... Algorithm 2 : Sequence Generation. |
| Open Source Code | Yes | Illustration videos and code are available on our project page: https://ramymounir.com/publications/pam. |
| Open Datasets | Yes | Datasets We perform evaluations on synthetic and real datasets. ... Additionally, we evaluate on real datasets of various types (e.g., protein sequences, text, vision)... Protein Net [7]... Moving MNIST [60], CLEVRER [68] as well as synthetically generated sequences of CIFAR [33] images. |
| Dataset Splits | No | The paper does not explicitly state training/validation/test dataset splits, only mentions training and testing. |
| Hardware Specification | No | PAM operates entirely on CPU. ... We specify the compute resources required for PAM (i.e., CPU) and plot a comparison of the time required by each method at different parameters in Figure 3 D. The paper only specifies 'CPU' without mentioning specific models or types. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify any software names with version numbers for libraries or programming languages. |
| Experiment Setup | Yes | D Implementation Details: For each model, we optimize a single set of hyperparameters for all the experiments. ... All η+ values in Equations 7 & 8 are set to 0.1. η B is set to 0.1, while η A is set to 0.0... The threshold for the δ function is set as a function of the SDR sparsity. For the transition function, we use a threshold of 0.8W... For the emission function, we use a threshold of 0.1W... For the t PC architecture, we use learning rate of 1e-4 for 800 learning iterations. |