We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization.

To solve this, we perform probabilistic reasoning over the depth of neural networks.

Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems.

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

Q2: Please summarize your review in 1-2 sentences The paper's main contribution are theoretical error bounds for a recently proposed low-rank tensor decomposition approach. The paper seems technically sound, but the results are somewhat incremental and may suffer from limited impact at NIPS.

Conditioning refers to the existence of multiple input signals.

Thanks to such progress, there have been growing interests in studying the expressive power of GNNs. One line of work does so by studying their ability to distinguish non-isomorphic graphs.

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper develops lower and upper bounds on the required rank of adjacency tensor factorizations to recover the adjacency tensor. This is, it investigates the problem of the minimal rank required to express the true underlying data via factorizations. These bounds are shown to of practical use by scaling RESCAL up. The paper is extremely well written and makes several interesting and important contributions.