Skip to main content

tcn package

Project description

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.

Experiments are done in PyTorch. If you find this repository helpful, please cite our work:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

astar_tcn-0.0.1-py3-none-any.whl (3.9 kB view details)

Uploaded Python 3

File details

Details for the file astar_tcn-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: astar_tcn-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 3.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.6

File hashes

Hashes for astar_tcn-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 88d0bdde2c67244c25a8c0839af9c68e9498287b341dc0423b82cc68b3bb998b
MD5 f03d27154b8e7aa4e631196f307dbcf5
BLAKE2b-256 6ff75547710b2267f79a31851e52069d9e26fb23b15aa057b59e850c46dd776b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page