Predictive Attractor Models
Authors: Ramy Mounir, Sudeep Sarkar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {ramy, sarkar}@usf.edu |
| 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. |