Perturbation Theory for the Information Bottleneck
Authors: Vudtiwat Ngampruetikorn, David J. Schwab
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our results on synthetic probability distributions, finding good agreement with the exact numerical solution near the onset of learning. |
| Researcher Affiliation | Academia | Vudtiwat Ngampruetikorn,* David J. Schwab Initiative for the Theoretical Sciences, The Graduate Center, CUNY *vngampruetikorn@gc.cuny.edu |
| Pseudocode | No | The provided text of the paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses 'synthetic probability distributions' and describes how these distributions are defined (e.g., Gaussian, exponential, Poisson, uniform functions), but it does not provide access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper describes the characteristics of the synthetic data distributions used (e.g., mean, variance, noise level) and refers to mathematical equations for the theory, but it does not provide specific experimental setup details like concrete hyperparameter values, training configurations, or system-level settings for a learning model. |