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Talk
Learned Optimizers that Scale and Generalize
Olga Wichrowska · Niru Maheswaranathan · Matthew Hoffman · Sergio Gómez Colmenarejo · Misha Denil · Nando de Freitas · Jascha Sohl-Dickstein

Mon Aug 07 12:33 AM -- 12:51 AM (PDT) @ Darling Harbour Theatre
in Deep learning 4: learning to learn »

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.

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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)

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