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Adaptive loss function formulation is an active area of research and has gained a great deal of popularity in recent years, following the success of deep learning.
An Automatic Method for Finding the Greatest or Least Value of a Function
H. H. Rosenbrock · 1960
Earlier work this paper cites.
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
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Two-point step size gradient methods
Jonathan Barzilai and Jonathan M Borwein · 1988
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Multitask Learning
Rich Caruana · 1998
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Task clustering and gating for bayesian multitask learning
Bart Bakker and Tom Heskes · 2003
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Learning deep architectures for ai
Yoshua Bengio · 2009
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Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Daphne Koller and Nir Friedman · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Transforming auto-encoders
Geoffrey E. Hinton, Alex Krizhevsky, and Sida D. Wang · 2011
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Deep architecture for traffic flow prediction
Wenhao Huang, Haikun Hong, Man Li, Weisong Hu, Guojie Song, and Kunqing Xie · 2013
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A literature survey of benchmark functions for global optimisation problems
Momin Jamil and Xin-She Yang · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
Cited alongside, same era.
Alireza Makhzani and Brendan J. Frey · 2013
Cited alongside, same era.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
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Wasserstein generative adversarial networks
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Group sparse autoencoder
Anush Sankaran, Mayank Vatsa, Richa Singh, and Angshul Majumdar · 2017
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Devansh Arpit, Yingbo Zhou, Hung Q. Ngo, and Venu Govindaraju · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Multi-objective optimization for self-adjusting weighted gradient in machine learning tasks
Conrado Miranda and Fernando Von Zuben · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Barbara Plank, Anders Søgaard, and Yoav Goldberg · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Cited alongside, same era.
Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, and Tieniu Tan · 2018
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On the convergence of stochastic gradient descent with adaptive stepsizes
Xiaoyu Li and Francesco Orabona · 2018
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Preconditioned stochastic gradient descent
Xi-Lin Li · 2018
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A general and adaptive robust loss function
Jonathan T. Barron · 2019
Closest in time.
Addressing the loss-metric mismatch with adaptive loss alignment
Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Ángel Bautista, Shih-Yu Sun, Carlos Guestrin, and Josh Susskind · 2019
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Barzilai–borwein-based adaptive learning rate for deep learning
Jinxiu Liang, Yong Xu, Chenglong Bao, Yuhui Quan, and Hui Ji · 2019
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Autoloss: Learning discrete schedules for alternate optimization
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing · 2019
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