Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Authors: Joshua Hanson, Maxim Raginsky
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that TCNs can approximate a large class of input-output maps having approximately finite 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 finite-dimensional state-space realizations (i.e., recurrent models). |
| Researcher Affiliation | Academia | Joshua Hanson University of Illinois Urbana, IL 61801 jmh4@illinois.edu Maxim Raginsky University of Illinois Urbana, IL 61801 maxim@illinois.edu |
| 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. |