… New York University and one of deep learning’s most vocal skeptics. … deep learning was
magic. Now people are realizing that it’s not magic.” Still, there’s no denying that deep learning …

… deep learning is so computationally expensive, we analyze its statistical and computational
scaling in theory. We show that deep learning is … burden of deep learning models is scaling …

… In our work, we consider the following questions to formalize the security of deep learning:
(1) “What is the minimal knowledge required to perform attacks against deep neural networks?…

… As the success of deep learning reaches more grounds, one … limits of deep learning. This
paper gives a first set of results proving that certain deep learning algorithms fail at learning …

… , Machine Learning (ML) and Deep Learning (DL). However, there certain limitation and flaws
that … This article points few of these limitations and flaws that presently are concerns of the …

… The goal of this paper is to characterize function distributions that deep learning can or …
-based deep learning and a non-universality result is proved for GD-based deep learning; this …

… the collection of massive amounts of labeled data, indispensable for training deep neural
networks [5]. Deep learning as we know it took off around 2010 [6] through the work of …

… This reinforces the described limits on the accuracy of algorithmically … deep learning, our
findings confirm the previously reported [36], [38] profound limitations of current deep learning …

… Supervised Learning and Deep Learning Almost all practical applications of machine learning
are based on supervised learning. Supervised learning is a process in which you train the …

… and limitations of deep learning. … limitations of deep learning, here evaluated in respect to
high-level machine intelligence. Experts in our sample named 40 limitations of deep learning. …