Timezone: »
Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal per-parameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse, optimization tasks capturing common properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on.
Author Information
Olga Wichrowska (Google Brain)
Niru Maheswaranathan (Stanford University)
Matthew Hoffman (DeepMind)
Sergio Gómez Colmenarejo (Google DeepMind)
Misha Denil (University of Oxford)
Nando de Freitas (DeepMind)
Jascha Sohl-Dickstein (Google Brain)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Poster: Learned Optimizers that Scale and Generalize »
Tue Aug 8th 08:30 AM -- 12:00 PM Room Gallery
More from the Same Authors
-
2020 Poster: Infinite attention: NNGP and NTK for deep attention networks »
Jiri Hron · Yasaman Bahri · Jascha Sohl-Dickstein · Roman Novak -
2020 Poster: Improving the Gating Mechanism of Recurrent Neural Networks »
Albert Gu · Caglar Gulcehre · Thomas Paine · Matthew Hoffman · Razvan Pascanu -
2019 Poster: Understanding and correcting pathologies in the training of learned optimizers »
Luke Metz · Niru Maheswaranathan · Jeremy Nixon · Daniel Freeman · Jascha Sohl-Dickstein -
2019 Poster: Guided evolutionary strategies: augmenting random search with surrogate gradients »
Niru Maheswaranathan · Luke Metz · George Tucker · Dami Choi · Jascha Sohl-Dickstein -
2019 Oral: Guided evolutionary strategies: augmenting random search with surrogate gradients »
Niru Maheswaranathan · Luke Metz · George Tucker · Dami Choi · Jascha Sohl-Dickstein -
2019 Oral: Understanding and correcting pathologies in the training of learned optimizers »
Luke Metz · Niru Maheswaranathan · Jeremy Nixon · Daniel Freeman · Jascha Sohl-Dickstein -
2019 Poster: Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning »
Natasha Jaques · Angeliki Lazaridou · Edward Hughes · Caglar Gulcehre · Pedro Ortega · DJ Strouse · Joel Z Leibo · Nando de Freitas -
2019 Oral: Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning »
Natasha Jaques · Angeliki Lazaridou · Edward Hughes · Caglar Gulcehre · Pedro Ortega · DJ Strouse · Joel Z Leibo · Nando de Freitas -
2019 Poster: The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study »
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith -
2019 Oral: The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study »
Daniel Park · Jascha Sohl-Dickstein · Quoc Le · Samuel Smith -
2018 Poster: Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks »
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington -
2018 Oral: Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks »
Lechao Xiao · Yasaman Bahri · Jascha Sohl-Dickstein · Samuel Schoenholz · Jeffrey Pennington -
2017 Poster: Learning to Learn without Gradient Descent by Gradient Descent »
Yutian Chen · Matthew Hoffman · Sergio Gómez Colmenarejo · Misha Denil · Timothy Lillicrap · Matthew Botvinick · Nando de Freitas -
2017 Poster: Input Switched Affine Networks: An RNN Architecture Designed for Interpretability »
Jakob Foerster · Justin Gilmer · Jan Chorowski · Jascha Sohl-Dickstein · David Sussillo -
2017 Talk: Input Switched Affine Networks: An RNN Architecture Designed for Interpretability »
Jakob Foerster · Justin Gilmer · Jan Chorowski · Jascha Sohl-Dickstein · David Sussillo -
2017 Poster: Parallel Multiscale Autoregressive Density Estimation »
Scott Reed · Aäron van den Oord · Nal Kalchbrenner · Sergio Gómez Colmenarejo · Ziyu Wang · Yutian Chen · Dan Belov · Nando de Freitas -
2017 Poster: On the Expressive Power of Deep Neural Networks »
Maithra Raghu · Ben Poole · Surya Ganguli · Jon Kleinberg · Jascha Sohl-Dickstein -
2017 Talk: Learning to Learn without Gradient Descent by Gradient Descent »
Yutian Chen · Matthew Hoffman · Sergio Gómez Colmenarejo · Misha Denil · Timothy Lillicrap · Matthew Botvinick · Nando de Freitas -
2017 Talk: On the Expressive Power of Deep Neural Networks »
Maithra Raghu · Ben Poole · Surya Ganguli · Jon Kleinberg · Jascha Sohl-Dickstein -
2017 Talk: Parallel Multiscale Autoregressive Density Estimation »
Scott Reed · Aäron van den Oord · Nal Kalchbrenner · Sergio Gómez Colmenarejo · Ziyu Wang · Yutian Chen · Dan Belov · Nando de Freitas