Optimization

Stacked U-Nets: A simple architecture for image segmentation

Stacked U-Nets are simple, easy-to-train neural architecture for image segmentation and other image-to-image regression tasks. SUNets attain state of the art performance and fast inference with very few parameters.

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Visualizing the Loss Landscape of Neural Nets

It is well known that certain neural network architectures produce loss functions that train easier and generalize better, but the reasons for this are not well understood. To understand this better, we explore the structure of neural loss functions using a range of visualization methods.

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Stabilizing GANs with Prediction

Adversarial networks are notoriously hard to train, and simple training methods often collapse. We present a simple modification to the standard training method that increases stability. The method is provably stable for a class of saddle-point problems, and improves performance of numerous GANs.

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PhasePack

PhasePack is a software library that implements a wide range of different phase retrieval algorithms and initialization methods. It can also produce comparisons between algorithms, and comes with empirical datasets for testing on real-world problems.

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Distributed Machine Learning

Classical machine learning methods, include stochastic gradient descent (aka backprop), work great on one machine, but don’t scale well to the cloud or cluster setting. We propose a variety of algorithmic frameworks for scaling machine learning across many workers.

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The stone transform: flexible compressed sensing

The stone transform enables images and videos to be under-sampled, and then reconstructed instantly at Nyquist rates, or at high resolution using compressed sensing.

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