# Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN)
This repository contains the experiments done in the work [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](#) by Shaojie Bai, J. Zico Kolter and Vladlen Koltun.
This repository contains the experiments done in the work [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://arxiv.org/abs/1803.01271) by Shaojie Bai, J. Zico Kolter and Vladlen Koltun.
We specifically target a comprehensive set of tasks that have been repeatedly used to compare the effectiveness of different recurrent networks, and evaluate a simple, generic but powerful (purely) convolutional network on the recurrent nets' home turf.
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@@ -11,7 +11,7 @@ Experiments are done in PyTorch. If you find this repository helpful, please cit
@article{BaiTCN2018,
author = {Shaojie Bai and J. Zico Kolter and Vladlen Koltun},
title = {An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling},
journal = {arXiv:XXXX.XXXXX},
journal = {arXiv:1803.01271},
year = {2018},
}
```
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@@ -32,7 +32,7 @@ This repository contains the benchmarks to the following tasks, with details exp
-**PennTreebank** [MEDIUM] char-level LM
-**text8** [LARGE] char-level LM
While some of the large datasets are not included in this repo, we use the [observations](#) package to download them, which can be easily installed using pip.
While some of the large datasets are not included in this repo, we use the [observations](https://github.com/edwardlib/observations) package to download them, which can be easily installed using pip.
