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Combining different models is a widely used paradigm in machine learning applications.
On the translocation of masses
Leonid V Kantorovich · 1942
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Original contribution: Stacked generalization
David H. Wolpert · 1992
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Learning complex, extended sequences using the principle of history compression
Jürgen Schmidhuber · 1992
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Bagging predictors
Leo Breiman · 1996
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Weight averaging for neural networks and local resampling schemes
Joachim Utans · 1996
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A brief introduction to boosting
Robert E. Schapire · 1999
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Gradient flows: in metric spaces and in the space of probability measures
L. Ambrosio, Nicola Gigli, and Giuseppe Savare · 2006
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Barycenters in the wasserstein space
Martial Agueh and Guillaume Carlier · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Regularized discrete optimal transport
Sira Ferradans, Nicolas Papadakis, Julien Rabin, Gabriel Peyré, and Jean-François Aujol · 2013
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Fast computation of wasserstein barycenters
Marco Cuturi and Arnaud Doucet · 2014
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Generative adversarial nets
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Very deep convolutional networks for large-scale image recognition, 2014
Karen Simonyan and Andrew Zisserman · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Meal: Multi-model ensemble via adversarial learning, 2018
Zhiqiang Shen, Zhankui He, and Xiangyang Xue · 2018
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Non-iterative knowledge fusion in deep convolutional neural networks
Mikhail Iu Leontev, Viktoriia Islenteva, and Sergey V Sukhov · 2018
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Insights on representational similarity in neural networks with canonical correlation, 2018
Ari S. Morcos, Maithra Raghu, and Samy Bengio · 2018
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Local sgd converges fast and communicates little
Sebastian Urban Stich · 2019
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Bayesian nonparametric federated learning of neural networks, 2019
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni · 2019
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How important is a neuron
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Communication-efficient learning of deep networks from decentralized data, 2016
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
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An investigation of how neural networks learn from the experiences of peers through periodic weight averaging
Joshua Smith and Michael Gashler · 2017
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Axiomatic attribution for deep networks, 2017
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Structured pruning of deep convolutional neural networks
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2017
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Mémoire sur la théorie des déblais et des remblais
Gaspard Monge
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Convergent learning: Do different neural networks learn the same representations?, 2016a
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft
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Kedar Dhamdhere, Mukund Sundararajan, and Qiqi Yan · 2019
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Importance estimation for neural network pruning
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit, 2019
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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