Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Authors: Joshua Hanson, Maxim Raginsky
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,346 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,525 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that TCNs can approximate a large class of input-output maps having approximately ๏ฌnite memory to arbitrary error tolerance. Furthermore, we derive quantitative approximation rates for deep Re LU TCNs in terms of the width and depth of the network and modulus of continuity of the original input-output map, and apply these results to input-output maps of systems that admit ๏ฌnite-dimensional state-space realizations (i.e., recurrent models). |
| Researcher Affiliation | Academia | Joshua Hanson University of Illinois Urbana, IL 61801 EMAIL Maxim Raginsky University of Illinois Urbana, IL 61801 EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets, thus no dataset access information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on experimental data splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. |