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ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and different noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate a highly-efficient solver that achieves state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.

Data Programming: A New Framework for Weakly Supervising Machine Learning Models

Date: Mar. 7, 2pm

Speaker: Alex Ratner

Location: Sycamore Conference Room (040-195)

Abstract: Today's state-of-the-art machine learning models require massive labeled training sets--which usually do not exist for real-world applications. Instead, I’ll discuss a newly proposed machine learning paradigm--data programming--and a system built around it, Snorkel, in which the developer focuses on writing a set of labeling functions, which are just scripts that programmatically label data. The resulting labels are noisy, but we model this as a generative process—learning, essentially, which labeling functions are more accurate than others—and then use this to train an end discriminative model (for example, a deep neural network in TensorFlow).  Given certain conditions, we show that this method has the same asymptotic scaling with respect to generalization error as directly-supervised approaches. Empirically, we find that by modeling a noisy training set creation process in this way, we can take potentially low-quality labeling functions from the user, and use these to train high-quality end models. We see this as providing a general framework for many weak supervision techniques, and at a higher level, as defining a new programming model for weakly-supervised machine learning systems.

Past Seminars

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Energy-efficient neuromorphic hardware and its use for deep neural networks

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