Nowadays integrated circuits (ICs) are underpinning all major information technology innovations including the current trends of artificial intelligence (AI).

We introduce Policy Optimization with Multiple Optima (POMO), anend-to-end approach forbuildingsuchaheuristic solver.POMO isapplicable to a wide range of CO problems.

Suppose l is -lipschitzand l-smoothforthefirstargument (i.e.

Modern neural networks often have great expressive power and can be trained to overfit the training data, while still achieving a good test performance.

Modern neural networks often have great expressive power and can be trained to overfit the training data, while still achieving a good test performance.

Specifically, instead of directly measuring the divergence with paired images, we train a reward model with the dataset we construct, consisting of nearly 51,000 images annotated with human preferences.

More challenging computational imaging applications, such as3D snapshot microscopywhichcompresses 3Dvolumes intosingle2Dimages, require ahighly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets).

Mathematically characterizing the implicit regularization induced by gradientbased optimization is a longstanding pursuit in the theory of deep learning. A widespread hope isthatacharacterization based onminimization ofnorms may apply, and a standard test-bed for studying this prospect is matrix factorization (matrix completion via linear neural networks). It is an open question whether normscanexplaintheimplicit regularization inmatrixfactorization.