HYPPO: Leveraging Prediction Uncertainty to Optimize Deep Learning Models for Science

In the computing world, optimization comes in all shapes and sizes, from data collection and processing to analysis, management, and storage. And calls for optimization tools applicable to a growing range of science and technology R&D efforts are emerging all the time.

New solutions can be found in deep learning modeling, which has attracted the attention of the scientific research community for applications ranging from studying climate change and cosmological evolution to tracking particle physics collisions and deciphering traffic patterns. This trend has prompted a parallel need for optimization tools that can enhance deep learning models and training to improve their predictive capabilities and accelerate time-consuming computer simulations.

Leveraging support from the Laboratory Directed Research and Development (LDRD) program at Lawrence Berkeley National Laboratory (Berkeley Lab), a team of researchers in the Computing Sciences Area has developed a new software tool for conducting hyperparameter optimization (HPO) of deep neural networks while taking into account the prediction uncertainty that arises from using stochastic optimizers for training the models. Hyperparameters define the architecture of a deep neural network and include the number of layers, nodes per layer, batch size, learning rate, etc. Dubbed “HYPPO,” this open-source package is designed to optimize the architectures of deep learning models specifically for scientific applications.

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