Decentralized deep learning framework in pytorch. Built to train models on thousands of volunteers across the world.

A Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)

Surrogate Modeling Toolbox

PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538

Hierarchical Mixture of Experts,Mixture Density Neural Network

Code for the paper "Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning"

Multi-Task Learning package built with tensorflow 2 (Multi-Gate Mixture of Experts, Cross-Stitch, Ucertainty Weighting)

A Pytorch implementation of Sparsely-Gated Mixture of Experts, for massively increasing the parameter count of language models

MoEL: Mixture of Empathetic Listeners

Machine learning code, derivatives calculation and optimization algorithms developed during the Machine Learning course at Universidade de Sao Paulo. All codes in Python, NumPy and Matplotlib with example in the end of file.

Using CCR to predict piezoresponse force microscopy datasets

This repository contains scripts for implementing various learning from expert architectures, such as mixture of experts and product of experts, and performing various experiments with these architectures.

Dataset, example models and demostration of our Interspeech 2019 paper

Hybrid small variant caller using Convolutional Neural Networks and Mixture-of-Experts

The implementation of mixtures for different tasks.

Implementation of Mixture of Experts paper

Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

Framework for Contextually Transferring Knowledge from Multiple Source Policies in Deep Reinforcement Learning