Gal Chechik

TL;DR: OASIS is an online dual approach using the passive-aggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost, which suggests that query independent similarity could be accurately learned even for large scale data sets that could not be handled before.

TL;DR: In this article, a multi-task network is proposed to combine clean and noisy data to improve the performance of image classification. But the clean data does not fully leverage the information contained in the clean set, and the clean annotations are used to reduce the noise in the large dataset before fine-tuning the network.

TL;DR: A formal definition of the general continuous IB problem is given and an analytic solution for the optimal representation for the important case of multivariate Gaussian variables is obtained, in terms of the eigenvalue spectrum.

TL;DR: An epistatic miniarray profile comprised of 100,000 pairwise, quantitative genetic interactions, including virtually all protein and small-molecule kinases and phosphatases as well as key cellular regulators is generated, finding an enrichment of positive genetic interactions between kinases, phosphatase, and their substrates.

TL;DR: Information about stimulus identity was somewhat reduced in single A1 and MGB neurons relative to single IC neurons, when information is measured using spike counts, latency, or temporal spiking patterns, but this difference was due to differences in firing rates.