We consider the problem of learning a low dimensional representation for compositional data.

The computational complexity of a property tester is the number of adjacency list entries it reads, denoted its queries . Many works in graph property testing focus on testing plain graphs that contain only the pure combinatorial information.

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

We analyze linear independence of rank one tensors produced by tensor powers of randomly perturbed vectors. This enables efficient decomposition of sums of high-order tensors. Our analysis builds upon Bhaskara et al.

We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks to the well-developed theory of information geometry, the reconstructed tensor is unique and always minimizes the KL divergence from an input tensor. We empirically show that Legendre decomposition can more accurately reconstruct tensors than other nonnegative tensor decomposition methods.

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

There are many clustering algorithms which perform well towards their aim of finding cohesive groups of instances.

Its tendency to improve accuracy and speed up training have established BN as a favorite technique in deep learning.

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

This paper presents a Gaussian process (GP) model to reconstruct high-resolution full velocity fields across the entire US.