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locuslab/TCN

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github.com/locuslab/TCN↗

TCN

TCN is a deep learning sequence framework and library for building temporal convolutional networks. It provides a toolkit for implementing purely convolutional architectures to model sequential data as an alternative to recurrent neural networks.

The project includes a sequence modeling benchmark suite designed to evaluate the accuracy and processing speed of architectures. This suite utilizes standardized tasks, including memory problems, digit classification, music, and language tasks, to quantify performance.

The framework covers a range of structural components for sequence processing, such as causal convolutional layers to prevent information leakage and dilated kernels to increase the receptive field. It also provides residual connection blocks for gradient stability and data pipelines for transforming raw time-series data into tensors.

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Features

  • Sequence Modeling Frameworks - Serves as a complete toolkit for implementing and testing time-series processing patterns using convolutional layers.
  • Dilated Convolutions - Utilizes dilated convolutions to exponentially increase the receptive field without increasing the parameter count.
  • Causal Convolutions - Implements causal convolutional layers that prevent information leakage from future to past in temporal sequences.
  • Sequence Learning Models - Provides capabilities for modeling sequential data across various time scales using convolutional layers.
  • Convolutional Sequence Models - Replaces recurrent neural networks with a stack of convolutional layers to allow parallel processing of sequences.
  • Temporal Convolutional Networks - Provides a dedicated library for building temporal convolutional networks as an alternative to recurrent neural networks.
  • Model Performance Benchmarking - Implements a performance evaluation framework to test model accuracy and speed across various sequence tasks.
  • Model Benchmarking Suites - Includes a collection of standardized tasks and datasets for evaluating the accuracy and efficiency of sequence models.
  • Residual Connection Implementations - Includes residual connection blocks to stabilize gradients and support deeper network architectures.
  • Time Series Forecasting - Provides convolutional structures designed to model temporal dependencies and predict future values in sequence data.
  • Model Evaluation and Benchmarking - Provides a benchmark suite to validate model accuracy and processing speed across standardized sequence tasks.
  • Time Series Tensor Pipelines - Ships pipelines that transform raw sequential data into formatted tensors for 1D convolutional processing.
  • Language Modeling - Convolutional and recurrent networks for sequence modeling.
  • Model Implementations - Temporal convolutional networks implementation.
4,525 stars·892 forks·Python·MIT·36 views

Star history

Star history chart for locuslab/tcnStar history chart for locuslab/tcn

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with TCN

These projects share indexed features with TCN. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph

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  • harvardnlp/annotated-transformerharvardnlp avatar

    harvardnlp/annotated-transformer

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    The Annotated Transformer is an educational resource that provides annotated code implementations of the Transformer architecture for sequence-to-sequence tasks, built with PyTorch. It serves as a learning tool for understanding attention mechanisms, multi-head parallel attention, and scaled dot-product attention through executable examples that walk through each component of the model. The project covers the full Transformer pipeline, including stacked encoder-decoder layers with residual connections and layer normalization, sinusoidal positional encoding for order-aware representation, and

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Frequently asked questions

What does locuslab/tcn do?

TCN is a deep learning sequence framework and library for building temporal convolutional networks. It provides a toolkit for implementing purely convolutional architectures to model sequential data as an alternative to recurrent neural networks.

What are the main features of locuslab/tcn?

The main features of locuslab/tcn are: Sequence Modeling Frameworks, Dilated Convolutions, Causal Convolutions, Sequence Learning Models, Convolutional Sequence Models, Temporal Convolutional Networks, Model Performance Benchmarking, Model Benchmarking Suites.

Which projects share features with locuslab/tcn?

Projects with overlapping indexed features include: pageman/sutskever-30-implementations — This project is a collection of deep learning research implementations and a reproduction kit designed to translate… openai/evals — Evals is a framework designed for automating, managing, and executing repeatable benchmarking suites to analyze the… awslabs/gluon-ts — GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability… harvardnlp/annotated-transformer — The Annotated Transformer is an educational resource that provides annotated code implementations of the Transformer… qwenlm/qwen — Qwen is a comprehensive framework for large language model development, serving, and deployment. It provides a… ashishpatel26/andrew-ng-notes — This project is a collection of structured study notes and notebooks serving as an educational resource for deep…