locuslab/TCN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4549 stars · 888 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PyTorch implementation of Temporal Convolutional Networks (TCN) with benchmarks from the paper 'An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling'. It includes experiment code for tasks like the adding problem, sequential MNIST, polyphonic music, and language modeling.

Use cases

• implement a temporal convolutional network in pytorch
• compare TCN against LSTM and GRU on sequence tasks
• run the adding problem benchmark
• train a model on sequential MNIST
• reproduce TCN language modeling results on PennTreebank
• learn how dilated causal convolutions work

When to choose

• you need a reference TCN implementation in PyTorch
• you want to benchmark convolutions vs recurrent networks on sequence modeling
• you are reproducing the Bai et al. 2018 paper

When to avoid

• you need a production-ready, actively maintained sequence modeling library
• you want a pip-installable package with an API rather than per-task scripts
• you need support for old PyTorch versions or modern features like mixed precision

Facets

library · maturity maintenance

machine-learning deep-learning benchmarking deep-learning machine-learning python temporal-convolutional-network pytorch sequence-modeling research-code benchmarks natural-language-processing

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