Phoster

Research and Development

Multitask Learning

Introduction

Clustering and Compositionality of Task Representations in a Neural Network Trained to Perform Many Cognitive Tasks by Guangyu R. Yang, H. Francis Song, William T. Newsome and Xiao-Jing Wang, Task Representations in Neural Networks Trained to Perform Many Cognitive Tasks by Guangyu R. Yang, Madhura R. Joglekar, H. Francis Song, William T. Newsome and Xiao-Jing Wang, Efficient and Robust Multi-task Learning in the Brain with Modular Task Primitives by Christian D. Marton, Guillaume Lajoie and Kanaka Rajan and Efficiently Identifying Task Groupings for Multi-task Learning by Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil and Chelsea Finn.

Multitask Learning

Multitask Learning by Rich Caruana, An Overview of Multi-task Learning in Deep Neural Networks by Sebastian Ruder, Multi-task Learning with Deep Neural Networks: A Survey by Michael Crawshaw, Learning Shared Representations in Multi-task Reinforcement Learning by Diana Borsa, Thore Graepel and John Shawe-Taylor and Multi-task Reinforcement Learning with Context-based Representations by Shagun Sodhani, Amy Zhang and Joelle Pineau.

Functional Specialization

The Functional Specialization of Visual Cortex Emerges from Training Parallel Pathways with Self-supervised Predictive Learning by Shahab Bakhtiari, Patrick Mineault, Timothy Lillicrap, Christopher Pack and Blake Richards and Brain-like Functional Specialization Emerges Spontaneously in Deep Neural Networks by Katharina Dobs, Julio Martinez, Alexander J. E. Kell and Nancy Kanwisher.

Generalization

Generalizing From a Few Examples: A Survey on Few-shot Learning by Yaqing Wang, Quanming Yao, James T. Kwok and Lionel M. Ni and Multi-task Generalization Using Practice for Distributed Deep Reinforcement Learning by Upasana Pattnaik.

Transfer Learning

A Survey on Transfer Learning by Sinno J. Pan and Qiang Yang, A Survey on Deep Transfer Learning by Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang and Chunfang Liu and Measuring and Harnessing Transference in Multi-task Learning by Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil and Chelsea Finn.

Domain Adaptation

Advances in Domain Adaptation Theory by Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban and Younès Bennani, Learning from Multiple Sources by Koby Crammer, Michael Kearns and Jennifer Wortman, A Theory of Learning from Different Domains by Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira and Jennifer Wortman Vaughan and An Introduction to Domain Adaptation and Transfer Learning by Wouter M. Kouw and Marco Loog.

Modularity

Modular Neural Networks: A Survey by Gasser Auda and Mohamed Kamel, Design and Evolution of Modular Neural Network Architectures by Bart L. M. Happel and Jacob M. J. Murre, Biologically Inspired Modular Neural Networks by Farooq Azam and A Review of Modularization Techniques in Artificial Neural Networks by Mohammed Amer and Tomás Maul.

Self-organization

Compositional Models: Multi-task Learning and Knowledge Transfer with Modular Networks by Andrey Zhmoginov, Dina Bashkirova and Mark Sandler.