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Poster
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Ondrej Bohdal · Yongxin Yang · Timothy Hospedales

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ Virtual
in Poster Session 1 »

Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters. Nevertheless, existing approaches are relatively expensive as they need to compute second-order derivatives and store a longer computational graph. This cost prevents scaling them to larger network architectures. We present EvoGrad, a new approach to meta-learning that draws upon evolutionary techniques to more efficiently compute hypergradients. EvoGrad estimates hypergradient with respect to hyperparameters without calculating second-order gradients, or storing a longer computational graph, leading to significant improvements in efficiency. We evaluate EvoGrad on three substantial recent meta-learning applications, namely cross-domain few-shot learning with feature-wise transformations, noisy label learning with Meta-Weight-Net and low-resource cross-lingual learning with meta representation transformation. The results show that EvoGrad significantly improves efficiency and enables scaling meta-learning to bigger architectures such as from ResNet10 to ResNet34.

Keywords: Deep Learning · Optimization · Graph Learning · Meta Learning · Few Shot Learning

Author Information

Ondrej Bohdal (University of Edinburgh)
Yongxin Yang (University of Edinburgh )
Timothy Hospedales (University of Edinburgh)

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