Code Repository for Liquid Time-Constant Networks (LTCs)

Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018

Liquid Structural State-Space Models

Efficient Python library for Extended LSTM with exponential gating, memory mixing, and matrix memory for superior sequence modeling.

Implementation of GateLoop Transformer in Pytorch and Jax

Pytorch implementation of Simplified Structured State-Spaces for Sequence Modeling (S5)

Sequential model for polyphonic music

Contains various architectures and novel paper implementations for Natural Language Processing tasks like Sequence Modelling and Neural Machine Translation.

The Reinforcement-Learning-Related Papers of ICLR 2019

Repo to reproduce the First-Explore paper results

Source code for "A Lightweight Recurrent Network for Sequence Modeling"

Python package for Arabic natural language processing

🔥 A minimal training framework for scaling FLA models

An implmentation of the AWD-LSTM in PyTorch

Audio and Music Synthesis with Machine Learning

Human Activity Recognition using Deep Learning on Spatio-Temporal Graphs

Tensorflow implementation of Long Short-Term Memory model for audio synthesis used for thesis

Deep, sequential, transductive divergence metric and domain adaptation for time-series classifiers

Minimal and efficient JAX implementation of the Mamba State Space Model in JAX/Flax. Inspired by 'Mamba: Linear-Time Sequence Modeling with Selective State Spaces,' this repo provides fast, scalable, and well-documented state-of-the-art sequence modeling tools.

The course studies fundamentals of distributed machine learning algorithms and the fundamentals of deep learning. We will cover the basics of machine learning and introduce techniques and systems that enable machine learning algorithms to be efficiently parallelized.