Multi-Task Learning as a Bargaining Game

Last update: Dec 26, 2022

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Overview

Nash-MTL

Official implementation of "Multi-Task Learning as a Bargaining Game".

Setup environment

conda create -n nashmtl python=3.9.7
conda activate nashmtl
conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
conda install pyg -c pyg -c conda-forge

Next -> cd to the unzipped folder i.e. cd RUN -> pip install -e .

QM9 Experiment

To run Nash-MTL:

cd experiments/quantum_chemistry
python trainer.py --method=nashmtl

To train using another MTL method simply replace 'nashmtl' with one of ['cagrad', 'pcgrad', 'mgda', 'ls', 'uw', 'scaleinvls'].

Owner

Aviv Navon

Data Scientist @ Aiola // PhD Student @ BIU

GitHub Repository

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Multi-Task Learning as a Bargaining Game

Related tags

Overview

Nash-MTL

Setup environment

QM9 Experiment

Owner

Aviv Navon

End-to-end beat and downbeat tracking in the time domain.

A denoising diffusion probabilistic model (DDPM) tailored for conditional generation of protein distograms

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

“英特尔创新大师杯”深度学习挑战赛赛道3：CCKS2021中文NLP地址相关性任务

PyTorch implementation of the implicit Q-learning algorithm (IQL)

I will implement Fastai in each projects present in this repository.

Modeling CNN layers activity with Gaussian mixture model

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

PyTorch implementation of the end-to-end coreference resolution model with different higher-order inference methods.

Dark Finix: All in one hacking framework with almost 100 tools

Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is accepted to ICCV2021.

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

SegNet-like Autoencoders in TensorFlow

An executor that loads ONNX models and embeds documents using the ONNX runtime.

A baseline code for VSPW

Node for thenewboston digital currency network.

Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

Multi-Task Learning as a Bargaining Game

Related tags

Overview

Nash-MTL

Setup environment

QM9 Experiment

Owner

Aviv Navon

End-to-end beat and downbeat tracking in the time domain.

A denoising diffusion probabilistic model (DDPM) tailored for conditional generation of protein distograms

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

“英特尔创新大师杯”深度学习挑战赛 赛道3：CCKS2021中文NLP地址相关性任务

PyTorch implementation of the implicit Q-learning algorithm (IQL)

I will implement Fastai in each projects present in this repository.

Modeling CNN layers activity with Gaussian mixture model

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

PyTorch implementation of the end-to-end coreference resolution model with different higher-order inference methods.

Dark Finix: All in one hacking framework with almost 100 tools

Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is accepted to ICCV2021.

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

SegNet-like Autoencoders in TensorFlow

An executor that loads ONNX models and embeds documents using the ONNX runtime.

A baseline code for VSPW

Node for thenewboston digital currency network.

Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

“英特尔创新大师杯”深度学习挑战赛赛道3：CCKS2021中文NLP地址相关性任务