Luke Metz

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All published works

Action	Title	Year	Authors
+ PDF Chat	OpenAI o1 System Card	2024	OpenAI NULL AUTHOR_ID Aaron Jaech Adam Tauman Kalai Adam Lerer Adam J. Richardson Ahmed El-Kishky A. M. Low Alec Helyar Aleksander Mądry
+ PDF Chat	GPT-4o System Card	2024	OpenAI NULL AUTHOR_ID A. M. Hurst Adam Lerer Adam P. Goucher Adam Perelman Aditya Ramesh Aidan Clark AJ Ostrow Akila Welihinda
+ PDF Chat	Transformer-Based Learned Optimization	2023	Erik Gärtner Luke Metz Mykhaylo Andriluka C. Daniel Freeman Cristian Sminchisescu
+	Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies	2023	Oscar Li J. Harrison Jascha Sohl‐Dickstein Virginia Smith Luke Metz
+	Practical tradeoffs between memory, compute, and performance in learned optimizers	2022	Luke Metz C. Daniel Freeman J. Harrison Niru Maheswaranathan Jascha Sohl‐Dickstein
+	Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models	2022	Aarohi Srivastava Abhinav Rastogi Abhishek S. Rao Abu Awal Shoeb Abubakar Abid Adam Fisch Adam R. Brown Adam Santoro Aditya Gupta Adrià Garriga-Alonso
+	A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases	2022	J. Harrison Luke Metz Jascha Sohl‐Dickstein
+	Discovered Policy Optimisation	2022	Chris Xiaoxuan Lu Jakub Grudzien Kuba Alistair Letcher Luke Metz Christian Schroeder de Witt Jakob Foerster
+	VeLO: Training Versatile Learned Optimizers by Scaling Up	2022	Luke Metz J. Harrison C. Daniel Freeman Amil Merchant Lucas Beyer James T. Bradbury Naman Agrawal Ben Poole Igor Mordatch Adam Roberts
+	Transformer-Based Learned Optimization	2022	Erik Gärtner Luke Metz Mykhaylo Andriluka C. Daniel Freeman Cristian Sminchisescu
+	General-Purpose In-Context Learning by Meta-Learning Transformers	2022	Louis Kirsch J. Harrison Jascha Sohl‐Dickstein Luke Metz
+ PDF Chat	Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies	2021	Paul Vicol Luke Metz Jascha Sohl‐Dickstein
+	Gradients are Not All You Need	2021	Luke Metz C. Daniel Freeman Samuel S. Schoenholz Tal Kachman
+	Training Learned Optimizers with Randomly Initialized Learned Optimizers.	2021	Luke Metz C. Daniel Freeman Niru Maheswaranathan Jascha Sohl‐Dickstein
+	Learn2Hop: Learned Optimization on Rough Landscapes	2021	Amil Merchant Luke Metz Sam Schoenholz Ekin D. Cubuk
+	Lyapunov Exponents for Diversity in Differentiable Games	2021	Jonathan Lorraine Paul Vicol Jack Parker-Holder Tal Kachman Luke Metz Jakob Foerster
+	Gradients are Not All You Need	2021	Luke Metz C. Daniel Freeman Samuel S. Schoenholz Tal Kachman
+	Training Learned Optimizers with Randomly Initialized Learned Optimizers	2021	Luke Metz C. Daniel Freeman Niru Maheswaranathan Jascha Sohl‐Dickstein
+	Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies	2021	Paul Vicol Luke Metz Jascha Sohl‐Dickstein
+	Reverse engineering learned optimizers reveals known and novel mechanisms	2020	Niru Maheswaranathan David Sussillo Luke Metz Ruoxi Sun Jascha Sohl‐Dickstein
+	Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves.	2020	Luke Metz Niru Maheswaranathan C. Daniel Freeman Ben Poole Jascha Sohl‐Dickstein
+	Towards GAN Benchmarks Which Require Generalization.	2020	Ishaan Gulrajani Colin Raffel Luke Metz
+	Towards GAN Benchmarks Which Require Generalization	2020	Ishaan Gulrajani Colin Raffel Luke Metz
+	Using a thousand optimization tasks to learn hyperparameter search strategies	2020	Luke Metz Niru Maheswaranathan Ruoxi Sun C. Daniel Freeman Ben Poole Jascha Sohl‐Dickstein
+	On Linear Identifiability of Learned Representations	2020	Geoffrey Roeder Luke Metz Diederik P. Kingma
+	Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian	2020	Jack Parker-Holder Luke Metz Cinjon Resnick Hengyuan Hu Adam Lerer Alistair Letcher Alex Peysakhovich Aldo Pacchiano Jakob Foerster
+	Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian	2020	Jack Parker-Holder Luke Metz Cinjon Resnick Hengyuan Hu Adam Lerer Alistair Letcher Alexander Peysakhovich Aldo Pacchiano Jakob Foerster
+	Parallel Training of Deep Networks with Local Updates	2020	Michael Laskin Luke Metz Seth Nabarro Mark Saroufim Badreddine Noune Carlo Luschi Jascha Sohl‐Dickstein Pieter Abbeel
+	Reverse engineering learned optimizers reveals known and novel mechanisms	2020	Niru Maheswaranathan David Sussillo Luke Metz Ruoxi Sun Jascha Sohl‐Dickstein
+	Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves	2020	Luke Metz Niru Maheswaranathan C. Daniel Freeman Ben Poole Jascha Sohl‐Dickstein
+	Learning to Predict Without Looking Ahead: World Models Without Forward Prediction	2019	Daniel Freeman David Ha Luke Metz
+	Using learned optimizers to make models robust to input noise	2019	Luke Metz Niru Maheswaranathan Jonathon Shlens Jascha Sohl‐Dickstein Ekin D. Cubuk
+	Learning an Adaptive Learning Rate Schedule	2019	Zhen Xu Andrew M. Dai Jonas Kemp Luke Metz
+	Learning to Predict Without Looking Ahead: World Models Without Forward Prediction	2019	C. Daniel Freeman Luke Metz David Ha
+	Learned optimizers that outperform SGD on wall-clock and test loss.	2018	Luke Metz Niru Maheswaranathan Jeremy Nixon C. Daniel Freeman Jascha Sohl‐Dickstein
+	Learned optimizers that outperform SGD on wall-clock and validation loss	2018	Luke Metz Niru Maheswaranathan Jeremy Nixon C. Daniel Freeman Jascha Sohl‐Dickstein
+	Learning Unsupervised Learning Rules	2018	Luke Metz Niru Maheswaranathan Brian Cheung Jascha Sohl‐Dickstein
+	Adversarial Spheres.	2018	Justin Gilmer Luke Metz Fartash Faghri Samuel S. Schoenholz Maithra Raghu Martin Wattenberg Ian Goodfellow
+	Meta-Learning Update Rules for Unsupervised Representation Learning	2018	Luke Metz Niru Maheswaranathan Brian Cheung Jascha Sohl‐Dickstein
+	Guided evolutionary strategies: Augmenting random search with surrogate gradients	2018	Niru Maheswaranathan Luke Metz George Tucker Dami Choi Jascha Sohl‐Dickstein
+	Understanding and correcting pathologies in the training of learned optimizers	2018	Luke Metz Niru Maheswaranathan Jeremy Nixon C. Daniel Freeman Jascha Sohl‐Dickstein
+	Adversarial Spheres	2018	Justin Gilmer Luke Metz Fartash Faghri Samuel S. Schoenholz Maithra Raghu Martin Wattenberg Ian Goodfellow
+	BEGAN: Boundary Equilibrium Generative Adversarial Networks	2017	David Berthelot Thomas Schumm Luke Metz
+	Discrete Sequential Prediction of Continuous Actions for Deep RL	2017	Luke Metz Julian Ibarz Navdeep Jaitly James Davidson
+	BEGAN: Boundary Equilibrium Generative Adversarial Networks	2017	David Berthelot Thomas Schumm Luke Metz
+	Unrolled Generative Adversarial Networks	2016	Luke Metz Ben Poole David Pfau Jascha Sohl‐Dickstein
+	Unrolled Generative Adversarial Networks	2016	Luke Metz Ben Poole David Pfau Jascha Sohl‐Dickstein
+	Unrolled Generative Adversarial Networks	2016	Luke Metz Ben Poole David Pfau Jascha Sohl‐Dickstein
+	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	2015	Alec Radford Luke Metz Soumith Chintala
+	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	2015	Alec Radford Luke Metz Soumith Chintala

Common Coauthors

Coauthor	Papers Together
Jascha Sohl‐Dickstein	26
C. Daniel Freeman	15
Niru Maheswaranathan	15
Ben Poole	7
J. Harrison	5
Samuel S. Schoenholz	5
Paul Vicol	4
Jakob Foerster	4
Adam Lerer	4
Colin Raffel	3
Alec Radford	3
Angela Jiang	3
Barret Zoph	3
Jeremy Nixon	3
Jack Parker-Holder	3
Tal Kachman	3
Ekin D. Cubuk	3
David Pfau	3
Alistair Letcher	3
Ishaan Gulrajani	3
Jason Phang	2
Kathleen Wood	2
Chelsea Voss	2
Fartash Faghri	2
Aleksander Mądry	2
Martin Wattenberg	2
Łukasz Kaiser	2
Hengyuan Hu	2
Charles J. Hudson	2
Jiayi Weng	2
Greg Brockman	2
Hyung Won Chung	2
Karan Singhal	2
Bob McGrew	2
Nitish Shirish Keskar	2
Ian Goodfellow	2
J. G. Parish	2
Joost Huizinga	2
Mykhaylo Andriluka	2
Amil Merchant	2
Ian Kivlichan	2
Giambattista Parascandolo	2
Christopher Hesse	2
Ilya Sutskever	2
Aldo Pacchiano	2
Maithra Raghu	2
Mianna Chen	2
Nikolas Tezak	2
Dimitris Tsipras	2
Soumith Chintala	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Learning to learn by gradient descent by gradient descent	2016	Marcin Andrychowicz Misha Denil Sergio Luis Suárez Gómez Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas	12
+	Understanding Short-Horizon Bias in Stochastic Meta-Optimization	2018	Yuhuai Wu Mengye Ren Renjie Liao Roger Grosse	8
+	Neural Optimizer Search with Reinforcement Learning	2017	Irwan Bello Barret Zoph Vijay Vasudevan Quoc V. Le	8
+ PDF Chat	ImageNet Large Scale Visual Recognition Challenge	2015	Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael S. Bernstein	7
+	Learning Gradient Descent: Better Generalization and Longer Horizons	2017	Kaifeng Lv Shunhua Jiang Jian Li	6
+	Practical Bayesian Optimization of Machine Learning Algorithms	2012	Jasper Snoek Hugo Larochelle Ryan P. Adams	6
+	On the difficulty of training Recurrent Neural Networks	2012	Razvan Pascanu Tomáš Mikolov Yoshua Bengio	5
+	Evolution Strategies as a Scalable Alternative to Reinforcement Learning	2017	Tim Salimans Jonathan Ho Xi Chen Ilya Sutskever	5
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	5
+ PDF Chat	No free lunch theorems for optimization	1997	David H. Wolpert William G. Macready	5
+	Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks	2015	Alec Radford Luke Metz Soumith Chintala	5
+	Searching for Activation Functions	2017	Prajit Ramachandran Barret Zoph Quoc V. Le	4
+	Learning to Optimize	2016	Ke Li Jitendra Malik	4
+	Evolved Policy Gradients	2018	Rein Houthooft Richard Y. Chen Phillip Isola Bradly C. Stadie Filip Wolski Jonathan Ho Pieter Abbeel	4
+	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms	2017	Xiao Han Kashif Rasul Roland Vollgraf	4
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	4
+ PDF Chat	Natural Evolution Strategies	2008	Daan Wierstra Tom Schaul Jan Peters Juergen Schmidhuber	4
+	Attention is All you Need	2017	Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Łukasz Kaiser Illia Polosukhin	4
+	Gradient-Based Optimization of Hyperparameters	2000	Yoshua Bengio	4
+	Using learned optimizers to make models robust to input noise	2019	Luke Metz Niru Maheswaranathan Jonathon Shlens Jascha Sohl‐Dickstein Ekin D. Cubuk	4
+	A method for unconstrained convex minimization problem with the rate of convergence o(1/k^2)	1983	Yurii Nesterov	4
+	The Loss Surfaces of Multilayer Networks	2015	Anna Choromanska Mikael Henaff Michaël Mathieu Gérard Ben Arous Yann LeCun	3
+	Unbiasing Truncated Backpropagation Through Time	2017	Yann Ollivier Corentin Tallec	3
+	Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling	2014	Jun‐Young Chung Çaǧlar Gülçehre Kyunghyun Cho Yoshua Bengio	3
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Tim Harley Timothy Lillicrap David Silver Koray Kavukcuoglu	3
+	On Empirical Comparisons of Optimizers for Deep Learning	2019	Dami Choi Christopher J. Shallue Zachary Nado Jaehoon Lee Chris J. Maddison George E. Dahl	3
+ PDF Chat	Learning Transferable Architectures for Scalable Image Recognition	2018	Barret Zoph Vijay Vasudevan Jonathon Shlens Quoc V. Le	3
+	Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves.	2020	Luke Metz Niru Maheswaranathan C. Daniel Freeman Ben Poole Jascha Sohl‐Dickstein	3
+	Adversarially Learned Inference	2016	Vincent Dumoulin Ishmael Belghazi Ben Poole Olivier Mastropietro Alex Lamb Martín Arjovsky Aaron Courville	3
+	Learning to Optimize Neural Nets	2017	Ke Li Jitendra Malik	3
+	Matching networks for one shot learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra	3
+ PDF Chat	Array programming with NumPy	2020	C. R. Harris K. Jarrod Millman Stéfan van der Walt Ralf Gommers Pauli Virtanen David Cournapeau Eric Wieser Julian Taylor Sebastian Berg Nathaniel J. Smith	3
+	Learned Optimizers that Scale and Generalize	2017	Olga Wichrowska Niru Maheswaranathan Matthew W. Hoffman Sergio Gómez Colmenarejo Misha Denil Nando de Freitas Jascha Sohl‐Dickstein	3
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel	3
+	One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling	2013	Ciprian Chelba Tomáš Mikolov Mike Schuster Qi Ge Thorsten Brants Phillipp Koehn Tony Robinson	3
+ PDF Chat	Identity Mappings in Deep Residual Networks	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	3
+	Review papers : The statistical basis of meta-analysis	1993	JL Fleiss	3
+	Pixel Recurrent Neural Networks	2016	Aäron van den Oord Nal Kalchbrenner Koray Kavukcuoglu	3
+ PDF Chat	Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles	2016	Mehdi Noroozi Paolo Favaro	3
+	Random gradient-free minimization of convex functions	2011	Yurii Nesterov	3
+	Structured Evolution with Compact Architectures for Scalable Policy Optimization	2018	Krzysztof Choromański Mark Rowland Vikas Sindhwani Richard E. Turner Adrian Weller	2
+	Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour	2017	Priya Goyal Piotr Dollár Ross Girshick Pieter Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He	2
+	Online Learning Rate Adaptation with Hypergradient Descent	2017	Atılım Güneş Baydin Robert Cornish David Martínez-Rubio Mark Schmidt Frank Wood	2
+	Prototypical Networks for Few-shot Learning	2017	Jake Snell Kevin Swersky Richard S. Zemel	2
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	2
+	Emergence of Locomotion Behaviours in Rich Environments	2017	Nicolas Heess Dhruva Tb Sriram Srinivasan Jay Lemmon Josh Merel Greg Wayne Yuval Tassa Tom Erez Ziyu Wang S. M. Ali Eslami	2
+	No More Pesky Learning Rates	2012	Tom Schaul Sixin Zhang Yann LeCun	2
+	Learning Unsupervised Learning Rules	2018	Luke Metz Niru Maheswaranathan Brian Cheung Jascha Sohl‐Dickstein	2
+	Energy-based Generative Adversarial Network	2016	Junbo Zhao Michaël Mathieu Yann LeCun	2