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Optimization is a ubiquitous modeling tool and is often deployed in settings which repeatedly solve similar instances of the same problem.
Using learned optimizers to make models robust to input noise
Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, and Ekin D Cubuk · 1906
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Iterative procedures for nonlinear integral equations
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A class of methods for solving nonlinear simultaneous equations
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Dynamic programming
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The theory of max-min, with applications
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Control of uncertain systems with a set-membership description of the uncertainty
Dimitri P Bertsekas · 1971
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Non-linear parametric optimization
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A method for unconstrained convex minimization problem with the rate of convergence o (1/k
Yurii Nesterov · 1983
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The principle of maximum entropy
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
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A layered network model of associative learning: learning to learn and configuration
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Modern homotopy methods in optimization
Layne T Watson and Raphael T Haftka · 1989
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A learning algorithm for continually running fully recurrent neural networks
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Sensitivity and stability analysis for nonlinear programming
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Backpropagation through time: what it does and how to do it
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Q-learning
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Use of genetic programming for the search of a new learning rule for neural networks
Samy Bengio, Yoshua Bengio, and Jocelyn Cloutier · 1994
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Fast exact multiplication by the hessian
Barak A Pearlmutter · 1994
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Residual algorithms: Reinforcement learning with function approximation
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An aggressive approach to loop unrolling
Jack W Davidson and Sanjay Jinturkar · 1995
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The helmholtz machine
Peter Dayan, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel · 1995
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On learning how to learn learning strategies
Jürgen Schmidhuber · 1995
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Bruno A Olshausen and David J Field · 1996
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An investigation of the gradient descent process in neural networks
Barak A Pearlmutter · 1996
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Multitask learning
Rich Caruana · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Theoretical models of learning to learn
Jonathan Baxter · 1998
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Neuroanimator: Fast neural network emulation and control of physics-based models
Radek Grzeszczuk, Demetri Terzopoulos, and Geoffrey Hinton · 1998
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The mnist database of handwritten digits
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Actor-critic algorithms
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Dynamic Programming and Optimal Control
Dimitri P. Bertsekas · 2000
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Evolution and design of distributed learning rules
Thomas Philip Runarsson and Magnus Thor Jonsson · 2000
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Atomic decomposition by basis pursuit
Scott Shaobing Chen, David L Donoho, and Michael A Saunders · 2001
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the" echo state network" approach , volume 5
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Envelope theorems for arbitrary choice sets
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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Optimally sparse representation in general (nonorthogonal) dictionaries via
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Sensitivity analysis of generalized equations
Alexander Shapiro · 2003
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Ingrid Daubechies, Michel Defrise, and Christine De Mol · 2004
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Optimal control theory: an introduction
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A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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Elements of information theory (wiley series in telecommunications and signal processing), 2006
Thomas M Cover and Joy A Thomas · 2006
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Nonsmooth equations in optimization: regularity, calculus, methods and applications , volume 60
Diethard Klatte and Bernd Kummer · 2006
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Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
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Intrinsic statistics on riemannian manifolds: Basic tools for geometric measurements
Xavier Pennec · 2006
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Bundle methods for machine learning
Alexander J. Smola, S. V. N. Vishwanathan, and Quoc V. Le · 2007
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Reverse-mode ad in a functional framework: Lambda the ultimate backpropagator
Barak A Pearlmutter and Jeffrey Mark Siskind · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
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Optimization algorithms on matrix manifolds
P-A Absil, Robert Mahony, and Rodolphe Sepulchre · 2009
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Implicit functions and solution mappings , volume 543
Asen L Dontchev and R Tyrrell Rockafellar · 2009
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A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
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Optimal transport: old and new , volume 338
Cédric Villani · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
John C. Duchi, Elad Hazan, and Yoram Singer · 2010
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Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
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Stabilite asyptotique pour des problemes de perturbations singulieres
P Habets · 2010
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Fast inference in sparse coding algorithms with applications to object recognition
Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun · 2010
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Finite-sample analysis of bellman residual minimization
Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, and Mohammad Ghavamzadeh · 2010
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Should one compute the temporal difference fix point or minimize the bellman residual? the unified oblique projection view
Bruno Scherrer · 2010
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Multi-agent learning with policy prediction
Chongjie Zhang and Victor R. Lesser · 2010
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Ranking via sinkhorn propagation
Ryan Prescott Adams and Richard S Zemel · 2011
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, and Eric Chu · 2011
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PILCO: A model-based and data-efficient approach to policy search
Marc Peter Deisenroth and Carl Edward Rasmussen · 2011
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Anderson acceleration for fixed-point iterations
Homer F Walker and Peng Ni · 2011
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Numerical continuation methods: an introduction , volume 13
Eugene L Allgower and Kurt Georg · 2012
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Generic methods for optimization-based modeling
Justin Domke · 2012
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
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Perturbation analysis of optimization problems
J Frédéric Bonnans and Alexander Shapiro · 2013
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Model predictive control
Eduardo F Camacho and Carlos Bordons Alba · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomás Mikolov, and Yoshua Bengio · 2013
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Learning stochastic inverses
Andreas Stuhlmüller, Jessica Taylor, and Noah D. Goodman · 2013
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Plug-and-play priors for model based reconstruction
Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Learning neural network policies with guided policy search under unknown dynamics
Sergey Levine and Pieter Abbeel · 2014
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
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Proximal algorithms
Neal Parikh and Stephen Boyd · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Deep learning
Ruslan Salakhutdinov · 2014
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin A. Riedmiller · 2014
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Efficiently solving repeated integer linear programming problems by learning solutions of similar linear programming problems using boosting trees
Ashis Gopal Banerjee and Nicholas Roy · 2015
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Convex optimization algorithms
Dimitri Bertsekas · 2015
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Convex optimization: Algorithms and complexity
Sébastien Bubeck et al · 2015
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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, and Yoshua Bengio · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, and Yuval Tassa · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Data-driven fluid simulations using regression forests
L’ubor Ladickỳ, SoHyeon Jeong, Barbara Solenthaler, Marc Pollefeys, and Markus Gross · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Autograd: Effortless gradients in numpy
Dougal Maclaurin, David Duvenaud, and Ryan P Adams · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Optimal transport for applied mathematicians
Filippo Santambrogio · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Theano: A python framework for fast computation of mathematical expressions
Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, et al · 2016
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Structured prediction energy networks
David Belanger and Andrew McCallum · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Tutorial on variational autoencoders
Carl Doersch · 2016
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Identity mappings in deep residual networks
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Modular meta-learning with shrinkage
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Journal of Machine Learning Research , 22:Art–No, 2021
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