4 accepted papers
Learning from multiple sources of labeled data presents unique challenges, including the cost of using many annotators in the training, test, and production time and their limited reliability. In this paper, we analyze the problem of label-efficient learning and propose a method for training a class…
Detection of the outliers is pivotal for any machine learning model deployed and operated in real-world. It is essential for the Deep Neural Networks that were shown to be overconfident with such inputs. Moreover, even deep generative models that allow estimation of the probability density of the in…
The problem of detecting the Out-of-Distribution (OoD) inputs is of paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable and often tend to make over-confident predictions for O…
Since the amount of sensitive information stored on smart-phones expands with the growth of their popularity, the need for reliable and usable authentication on these devices increases. Constant reauthentication requests of standard methods, such as PINs, passwords, and swipe patterns, annoy many us…