Using a thousand optimization tasks to learn hyperparameter search strategies

• URL: http://arxiv.org/abs/2002.11887v3
• Date: Wed, 1 Apr 2020 00:35:05 GMT
• Title: Using a thousand optimization tasks to learn hyperparameter search strategies
• Authors: Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein
• Abstract summary: We present TaskSet, a dataset of neural tasks for use in training and evaluating neurals. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional networks, to variational autoencoders, to non-volume preserving flows on a variety of datasets.
• Score: 53.318615663332274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variational autoencoders, to non-volume preserving flows on a variety of datasets. As an example application of such a dataset we explore meta-learning an ordered list of hyperparameters to try sequentially. By learning this hyperparameter list from data generated using TaskSet we achieve large speedups in sample efficiency over random search. Next we use the diversity of the TaskSet and our method for learning hyperparameter lists to empirically explore the generalization of these lists to new optimization tasks in a variety of settings including ImageNet classification with Resnet50 and LM1B language modeling with transformers. As part of this work we have opensourced code for all tasks, as well as ~29 million training curves for these problems and the corresponding hyperparameters.

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