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From Communications of the ACM

Technical Perspective: Why Don't Today's Deep Nets Overfit to Their Training Data?

"Understanding Deep Learning (Still) Requires Rethinking Generalization," Chiyuan Zhang, et al., brings a fundamental new theoretical challenge: Why don't today's...

Learning Topic Models - Provably and Efficiently
From Communications of the ACM

Learning Topic Models - Provably and Efficiently

This article shows that some new theoretical algorithms that have provable guarantees can be adapted to yield highly practical tools for topic modeling.

Computational Complexity and Information Asymmetry in Financial Products
From Communications of the ACM

Computational Complexity and Information Asymmetry in Financial Products

Securitization of cash flows using financial derivatives transformed the financial industry over the last three decades. Derivatives...

Geometry, Flows, and Graph-Partitioning Algorithms
From Communications of the ACM

Geometry, Flows, and Graph-Partitioning Algorithms

"Graph partitioning" refers to a family of computational problems in which the vertices of a graph have to be partitioned...
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