Word Sense Disambiguation (WSD)4 [36] is a dataset to identify the most appropriate sense (out of three given senses) of the word "president" in a given paragraph.

Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priorityelement.

In this paper, we focus on SCI recovery algorithms that employ untrained neural networks (UNNs), such as deep image prior (DIP), to model source structure.

When training complex neural networks, memory usage can be an important bottleneck.

We are interested in a framework of online learning with kernels for lowdimensional, but large-scale and potentially adversarial datasets. We study the computational and theoretical performance of online variations of kernel Ridge regression.

Asynchronous Gibbs sampling has been recently shown to be fast-mixing and an accurate method for estimating probabilities of events on a small number of variables of a graphical model satisfying Dobrushin's condition [DSOR16].

Wegivethe first efficient algorithms proven to succeed in the correlated Erdös-Rényi model (Pedarsani and Grossglauser, 2011). Specifically, we give apolynomial time algorithm for thegraphsimilarity/hypothesis testingtaskwhich worksforeveryconstant level of correlation between the two graphs that can be arbitrarily close to zero. We also give a quasi-polynomial (nO(logn) time) algorithm for thegraph matching task of recovering the permutation minimizing the symmetric difference in this model.

A recent line of work initiated by Dworket al. [9] posed the question: Can we designgeneralpurpose algorithms for ensuring generalization in the presence of post-selection?

We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i.e., consistency.

Let k be the collection of distributions over the alphabet[k]:= {1,2,...,k}, and [k] be the set of finite sequences over[k].