Sensory Integration and Density Estimation
Authors: Joseph G Makin, Philip N. Sabes
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove here an analytical connection between these seemingly different tasks, density estimation and sensory integration; that the former implies the latter for the model used in [2]; but that this does not appear to be true for all models. and Here we prove analytically that successful density estimation in certain models, including that of [2], will necessarily satisfy the information-retention criterion. |
| Researcher Affiliation | Academia | Joseph G. Makin and Philip N. Sabes Center for Integrative Neuroscience/Department of Physiology University of California, San Francisco San Francisco, CA 94143-0444 USA makin, sabes @phy.ucsf.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of code related to the analytical proof presented in this paper. |
| Open Datasets | No | The paper describes data generation for related empirical work but does not provide access information (link, DOI, or explicit public dataset name with citation) for a dataset used in its own analysis. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments requiring training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers for its analytical work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameter values or training configurations. |