Large amounts of labeled data are typically required to train deep learning models.

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.

Coordinated counterterrorism raids across Germany and Austria, with Mossad and European agencies working together, uncover Hamas network planning attacks on Israeli and Jewish civilians.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this contribution, we provide a rigorous justification of these approaches for a two-layers neural network model called the committee machine.

We propose and study the problem of distribution-preserving lossy compression.

Graph-structured data arise in wide applications, such as computer vision, bioinfor-matics, and social networks. Quantifying similarities among graphs is a fundamental problem.

In diverse world of deep learning research has given rise to numerous architectures for neural networks (convolutional ones, long short term memory ones, etc). However, to this date, the underlying training algorithms for neural networks are still stochastic gradient descent (SGD) and its heuristic variants. In this paper, we address the problem of designing a new algorithm that has provably faster running time than the best known result for SGD.

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