Scholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora.
Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM).
With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes.
We propose a number of methods for neural network architecture design to improve the performance with fixed-point calculations.
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization.
We consider the problem of optimizing an unknown function given as an oracle over a mixed-integer box-constrained set.
Despite the recent improvement of learning-based image processing methods in image quality, there lacks enough analysis into their interactions and characteristics under a realistic setting of the mixture problem of demosaicing, denoising and SR.
The second one (DTHB) is a multi-year effort to express the linguistic features of the Hebrew bible in a text database, which is still growing in detail and sophistication.
Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success.