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We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Frederik Kratzert, Daniel Klotz, Mathew Herrnegger, Alden K. Sampson, Sepp Hochreiter, and Grey S. Nearing · 1944
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The HBV model – its structure and applications
Sten Bergström · 1992
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Development and test of the distributed hbv-96 hydrological model
Göran Lindström, Barbro Johansson, Magnus Persson, Marie Gardelin, and Sten Bergström · 1997
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Vapnik-Chervonenkis dimension of recurrent neural networks
Pascal Koiran and Eduardo D. Sontag · 1998
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Learning to learn using gradient descent
Sepp Hochreiter, A. Steven Younger, and Peter R. Conwell · 2001
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Vapnik-Chervonenkis dimension of neural nets
Peter L. Bartlett and Wolfgang Maass · 2003
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2004
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The effective rank: A measure of effective dimensionality
Olivier Roy and Martin Vetterli · 2007
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Stationarity is dead: whither water management?
Paul C. D. Milly, Julio Betancourt, Malin Falkenmark, Robert M. Hirsch, Zbigniew W. Kundzewicz, Dennis P. Lettenmaier, and Ronald J. Stouffer · 2008
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Application of a conceptual hydrologic model in teaching hydrologic processes
Amir Aghakouchak and Emad Habib · 2010
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Hydrological modelling in a changing world
Murray C. Peel and Günter Blöschl · 2011
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A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate–accounting for changing watershed behavior
Riddhi Singh, Thorsten Wagener, Katie Van Werkhoven, Michael E. Mann, and Robert Crane · 2011
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Teaching hydrological modeling with a user-friendly catchment-runoff-model software package
Jan Seibert and Marc JP Vis · 2012
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Daymet: Daily surface weather on a 1 km grid for north america, 1980-2008
Peter E. Thornton, Michele M. Thornton, Benjamin W. Mayer, Nate Wilhelmi, Yaxing Wei, Ranjeet Devarakonda, and R. Cook · 2012
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Revisiting natural gradient for deep networks
Razvan Pascanu and Yoshua Bengio · 2013
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Atmospheric circulation as a source of uncertainty in climate change projections
Theodore G. Shepherd · 2014
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Global warming and changes in drought
Kevin E. Trenberth, Aiguo Dai, Gerard Van Der Schrier, Philip D. Jones, Jonathan Barichivich, Keith R. Briffa, and Justin Sheffield · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas · 2016
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Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Ferrite inductor models for switch-mode power supplies analysis and design
G. Di Capua, N. Femia, K. Stoyka, M. Lodi, A. Oliveri, and M. Storace · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
Risto Vuorio, Shao-Hua Sun, Hexiang Hu, and Joseph J. Lim · 2019
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Fast Context Adaptation via Meta-Learning
Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
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Cross-domain few-shot learning by representation fusion
Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David Kreil, Michael Kopp, Günter Klambauer, and Sepp Hochreiter · 2020
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High-dimensional dynamics of generalization error in neural networks
Madhu S. Advani, Andrew M. Saxe, and Haim Sompolinsky · 2020
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Modular meta-learning with shrinkage
Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, and Nando de Freitas · 2020
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Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Optimization as a Model for Few-Shot Learning
Sachin Ravi and Hugo Larochelle · 2017
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Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Gradient descent happens in a tiny subspace
Guy Gur-Ari, Daniel A. Roberts, and Ethan Dyer · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Yoonho Lee and Seungjin Choi · 2018
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Measuring the Intrinsic Dimension of Objective Landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Martin Gauch, Daniel Klotz, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Jimmy Lin · 2020
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Generalization bounds for deep convolutional neural networks
Philip M. Long and Hanie Sedghi · 2020
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Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2020
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Few-shot domain adaptation by causal mechanism transfer
Takeshi Teshima, Issei Sato, and Masashi Sugiyama · 2020
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Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2020
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A unified few-shot classification benchmark to compare transfer and meta learning approaches
Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, and Hugo Larochelle · 2021
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Continuous-time system identification with neural networks: Model structures and fitting criteria
Marco Forgione and Dario Piga · 2021
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How many degrees of freedom do we need to train deep networks: a loss landscape perspective
Brett W. Larsen, Stanislav Fort, Nic Becker, and Surya Ganguli · 2021
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Fast adaptation with linearized neural networks
Wesley Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Wilson, and Andreas Damianou · 2021
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A retrospective on hydrological modelling based on half a century with the hbv model
Jan Seibert and Sten Bergström · 2021
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A diffusion theory for deep learning dynamics: Stochastic gradient descent exponentially favors flat minima
Zeke Xie, Issei Sato, and Masashi Sugiyama · 2021
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On the adaptation of recurrent neural networks for system identification
Marco Forgione, Aneri Muni, Dario Piga, and Marco Gallieri · 2022
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Climate Change 2022: Impacts, Adaptation and Vulnerability
Hans-O. Pörtner, Debra C. Roberts, Helen Adams, Carolina Adler, Paulina Aldunce, Elham Ali, Rawshan Ara Begum, Richard Betts, Rachel Bezner Kerr, Robbert Biesbroek, et al · 2022
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