… Deep learning allows computational models that are composed of multiple processing
layers to learn representations of data with multiple levels of abstraction. These methods have …

… model architecture for processing images that was inspired by the structure of the mammalian
visual system and later became the basis for the modern convolutional network (LeCun et …

… Deep learning (DL) and reinforcement learning (RL) … in the “Deep Learning and Reinforcement
Learning” session of … advances of deep learning and reinforcement learning algorithms. …

… The term deep learning refers either to networks with many layers (as in this work) but
sometimes it is used for unsupervised pretraining which allows for wellperforming deeper …

• It is easy to generate an unbiased example at the leaf nodes, so we can see what kinds of
data the network believes in.• It is hard to infer the posterior distribution over all possible …

… We reviewed the basic concepts and some of the breakthrough achievements of deep
learning several years ago.Here we briefly describe the origins of deep learning, describe a few …

… 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 …

… We study the problem of stochastic optimization for deep learning in the parallel computing
… We empirically demonstrate that in the deep learning setting, due to the existence of many …

… There is no opposition between graphical models and deep learning. Many deep
learning models are formulated as factor graphs Some graphical models use deep …

… attempting to generalize (structured) deep neural models to non-… examples of geometric
deep-learning problems and present … Overview of deep learning Deep learning refers to learning …