#sequence modeling

Keras Temporal Convolutional Network provides a compelling option to LSTM and GRU for extended time series tasks like Sequential MNIST and Word-level PTB. It is known for its enhanced memory retention, superior performance, and stable gradients, supported by parallel architecture and flexible receptive fields. Suitable for TensorFlow 2.9 to 2.17, Keras TCN is designed for cross-platform installations with flexible configurations, including skip connections and batch normalization. Discover its potential through practical implementations like adding tasks and copy memory tasks for comprehensive sequence modeling.

This repository offers insights into structured state space models, including S4 and its variants, for sequence modeling. It comprises implementations, training scripts, and kernel optimizations for long sequence processing with PyTorch. Features include customizable Hydra configurations, flexible integration with other repositories, and sophisticated generation capabilities. It supports model training and testing on datasets like MNIST, CIFAR, and WikiText, utilizing CUDA and Pykeops kernels for improved performance.

This catalog provides a wide range of Mamba-related resources, including papers, tutorials, and videos for all user levels. It acts as a detailed reference point for understanding Mamba, integrating recent research, practical guides, and instructional videos. This resource encourages community collaboration to boost Mamba skills and promote shared learning. Topics covered range from architecture and theoretical analysis to practical applications in vision and language.

This PyTorch-based Mamba architecture offers a straightforward and readable implementation with equivalent numerical outputs to the official model in both forward and backward passes. While not optimized for speed, it allows for easy comprehension and possible modifications such as parameter initialization. Access the demo file for practical examples of its application in prompt completions.

Discover the PyxLSTM Python library, an advanced implementation of xLSTM that improves upon traditional LSTM with innovations like exponential gating and matrix memory for enhanced sequence modeling. It provides tools for model definition, training, and data processing within language modeling and text generation tasks. Thoroughly tested and documented, PyxLSTM supports scalar and matrix LSTM types and is compatible with Python 3.6 to 3.10, allowing for easy integration into varied projects.