Code Repository for Liquid Time-Constant Networks (LTCs)
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Updated
Jun 3, 2024 - Python
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
PyxLSTM is a Python library that provides an efficient and extensible implementation of the Extended Long Short-Term Memory (xLSTM) architecture. xLSTM enhances the traditional LSTM by introducing exponential gating, memory mixing, and a matrix memory structure, enabling improved performance and scalability for sequence modeling tasks.
The Reinforcement-Learning-Related Papers of ICLR 2019
Contains various architectures and novel paper implementations for Natural Language Processing tasks like Sequence Modelling and Neural Machine Translation.
Implementation of GateLoop Transformer in Pytorch and Jax
Python package for Arabic natural language processing
Sequential model for polyphonic music
Repo to reproduce the First-Explore paper results
An implmentation of the AWD-LSTM in PyTorch
VOGUE: Variable Order HMM with Duration
Source code for "A Lightweight Recurrent Network for Sequence Modeling"
Pytorch implementation of Simplified Structured State-Spaces for Sequence Modeling (S5)
Deep, sequential, transductive divergence metric and domain adaptation for time-series classifiers
Caption Images with Machine Learning
Computer vision tools for analyzing behavioral data, including complex event detection in videos.
Sentiment analysis performed using a pre-trained BERT model on Mac Miller's complete discography.
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.
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