multi-task-machine-learning-papers

KO | EN

gitlite — search

#javascript #hacktoberfest #react #llm #go #rust #docker #nodejs #cli #kubernetes #vue #pytorch #ios #mcp

multi-task-machine-learning-papers

(★ 36)

A list of multi-task machine learning papers.

multi-task-machine-learning-papers 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Multi-task Machine Learning Paper List

A list of papers that are relevant to multi-task machine learning. Inspired by
[awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers) and 
[awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning).

## Table of Contents

* [Hard Parameter Sharing](#hard-parameter-sharing)
 * [Soft Parameter Sharing](#soft-parameter-sharing)
 * [Neural Architecture Search](#neural-architecture-search)
 * [Others](#others)

## Hard Parameter Sharing

* [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://arxiv.org/pdf/1711.05769.pdf).  
   Arun Mallya, Svetlana Lazebnik.  
   CVPR 2018.
 * [Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back](https://arxiv.org/pdf/1803.04062.pdf).  
   Elliot Meyerson, Risto Miikkulainen.  
   ICML 2018.
 * [Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering](https://arxiv.org/pdf/1711.00108.pdf).  
   Elliot Meyerson, Risto Miikkulainen.  
   ICLR 2018.
 * [Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning](https://arxiv.org/pdf/1711.01239.pdf).  
   Clemens Rosenbaum, Tim Klinger, Matthew Riemer.  
   ICLR 2018.
 * [Unifying and Merging Well-trained Deep Neural Networks for Inference Stage](https://arxiv.org/pdf/1805.04980.pdf).  
   Yi-Min Chou, Yi-Ming Chan, Jia-Hong Lee, Chih-Yi Chiu, Chu-Song Chen.  
   IJCAI-ECAI 2018.
 * [Multi-task Sequence to Sequence Learning](https://arxiv.org/pdf/1511.06114.pdf).  
   Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser.  
   ICLR 2016.

## Soft Parameter Sharing

* [Cross-stitch Networks for Multi-task Learning](https://arxiv.org/pdf/1604.03539.pdf).  
   Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, Martial Hebert.  
   CVPR 2016.

## Neural Architecture Search

* [Evolutionary Architecture Search For Deep Multitask Networks](https://arxiv.org/pdf/1803.03745.pdf).  
   Jason Liang, Elliot Meyerson, Risto Miikkulainen.  
   Genetic and Evolutionary Computation Conference (GECCO) 2018.
 * [Transfer Learning to Learn with Multitask Neural Model Search](https://arxiv.org/pdf/1710.10776.pdf).  
   Catherine Wong, Andrea Gesmundo.  
   arXiv preprint 2017.

## Others

* [AutoLoss: Learning Discrete Schedules for Alternate Optimization](https://arxiv.org/pdf/1810.02442.pdf).  
   Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing.  
   arXiv preprint 2018.
 * [Dynamic Task Prioritization for Multitask Learning](http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Focus_on_the_ECCV_2018_paper.pdf).  
   Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, Li Fei-Fei.  
   ECCV 2018.
 * [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://arxiv.org/pdf/1705.07115.pdf).  
   Alex Kendall, Yarin Gal, Roberto Cipolla.  
   CVPR 2018.
 * [An Overview of Multi-Task Learning in Deep Neural Networks](https://arxiv.org/pdf/1706.05098.pdf).  
   Sebastian Ruder.  
   arXiv preprint 2017.
 * [A Survey on Multi-Task Learning](https://arxiv.org/pdf/1707.08114.pdf).  
   Yu Zhang, Qiang Yang.  
   arXiv preprint 2017.

multi-task-machine-learning-papers

multi-task-machine-learning-papers 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

multi-task-machine-learning-papers

multi-task-machine-learning-papers 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README