Fetching the paper…
Reading the bibliography…
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly.
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 1901
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
Earlier work this paper cites.
Cryptographic limitations on learning boolean formulae and finite automata
Michael Kearns and Leslie Valiant · 1994
Earlier work this paper cites.
Sharper bounds for gaussian and empirical processes
Michel Talagrand · 1994
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
Earlier work this paper cites.
Noise-tolerant learning, the parity problem, and the statistical query model
Avrim Blum, Adam Kalai, and Hal Wasserman · 2003
Earlier work this paper cites.
Is the mind massively modular?
Richard Samuels · 2006
Earlier work this paper cites.
Cryptographic hardness for learning intersections of halfspaces
Adam R Klivans and Alexander A Sherstov · 2009
Earlier work this paper cites.
On lattices, learning with errors, random linear codes, and cryptography
Oded Regev · 2009
Cited alongside, same era.
The next generation of the penn world table
Robert C Feenstra, Robert Inklaar, and Marcel P Timmer · 2015
Cited alongside, same era.
Finding correlations in subquadratic time, with applications to learning parities and the closest pair problem
Gregory Valiant · 2015
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al · 2017
Cited alongside, same era.
Lukasz Kaiser, Aidan N Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Later among the works it cites.
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, and Clement Hongler · 2018
Later among the works it cites.
Massively multilingual neural machine translation
Roee Aharoni, Melvin Johnson, and Orhan Firat · 2019
Later among the works it cites.
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
Later among the works it cites.
Massively multilingual neural machine translation in the wild: Findings and challenges
Ankur Bapna, Colin Andrew Cherry, Dmitry Dima Lepikhin, George Foster, Maxim Krikun, Melvin Johnson, Mia Chen, Naveen Ari, Orhan Firat, Wolfgang Macherey, et al · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Multitasking capability versus learning efficiency in neural network architectures
Sebastian Musslick, Andrew Saxe, Kayhan Özcimder, Biswadip Dey, Greg Henselman, and Jonathan D Cohen · 2017
Cited alongside, same era.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Cited alongside, same era.
On the complexity of learning neural networks
Le Song, Santosh Vempala, John Wilmes, and Bo Xie · 2017
Cited alongside, same era.
A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
Cited alongside, same era.
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang
Cited in the paper.
Later among the works it cites.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabás Póczos, and Aarti Singh · 2019
Later among the works it cites.
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
Later among the works it cites.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
Later among the works it cites.
Small and practical bert models for sequence labeling
Henry Tsai, Jason Riesa, Melvin Johnson, Naveen Arivazhagan, Xin Li, and Amelia Archer · 2019
Later among the works it cites.