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We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process.
Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
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Lampert, C., Nickisch, H., and Harmeling, S · 2009
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Jia, Y · 2013
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Kingma, D. P. and Ba, J · 2014
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Asymmetric multi-task learning based on task relatedness and confidence
Lee, G., Yang, E., and Hwang, S · 2016
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Trace Norm Regularised Deep Multi-Task Learning
Yang, Y. and Hospedales, T. M · 2016
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Trace Norm Regularised Deep Multi-Task Learning
Yang, Y. and Hospedales, T. M · 2016
Later among the works it cites.
Learning what to share between loosely related tasks
Ruder, S., Bingel, J., Augenstein, I., and Søgaard, A · 2017
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Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Yang, Y. and Hospedales, T · 2017
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al · 2016
Cited alongside, same era.
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Yang, Y. and Hospedales, T · 2017
Closest in time.