CAREER: Variational Inference for Resource-Efficient Learning

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The power of Deep Learning (DL) comes with enormous energy and storage costs due to massive data needs and parameter-rich models. For example, recent models for natural language generation contain more than a hundred billion parameters and require huge amounts of training data. Training such a model can entail nearly five times the lifetime carbon dioxide emissions of the average American car. This project develops a holistic approach to resource-efficient DL based on a common set of methodologies: DL models and algorithms are viewed through the lens of information theory, making it possible to formally quantify and minimize the required resources. Outcomes of this project include new methods for the compression of both models (neural networks) and data (images and video), as well as new training algorithms for DL that reduce data requirements and improve runtime efficiency. These research activities will also inform summer teaching activities for under-represented students, lead to new open-source software for resource-efficient machine learning, as well as workshops and symposia on neural compression and statistical machine learning. In more detail, the project approaches resource-efficient machine learning from the perspective of variational inference (VI) and contains three thrusts that focus on different inefficiencies: (A) bandwidth inefficiency: a model's inefficient representation of data or parameters, (B) data inefficiency: a model's extensive need for training data, and (C) runtime inefficiency: a learning or inference algorithm's inability to produce desired answers within a given computational time budget. Thrust A draws on the connection between VI and rate-distortion theory to derive new neural compression algorithms with improved compression performance. Thrust B designs informative priors for effective learning with limited data in non-stationary environments. Finally, thrust C develops highly scalable training algorithms for Bayesian neural networks that hybridize Markov Chain Monte Carlo and VI, trading-off precision for convergence speed. The project contains applications from the domains of image and video compression as well as climate science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.