A Brief Tour of Deep Learning from a Statistical Perspective
Eric Nalisnick, Padhraic Smyth, Dustin Tran
Abstract
We expose the statistical foundations of deep learning with the goal of facilitating conversation between the deep learning and statistics communities. We highlight core themes at the intersection; summarize key neural models, such as feedforward neural networks, sequential neural networks, and neural latent variable models; and link these ideas to their roots in probability and statistics. We also highlight research directions in deep learning where there are opportunities for statistical contributions.
Topics & Concepts
Deep learningArtificial intelligenceArtificial neural networkComputer sciencePerspective (graphical)Latent variableConversationStatistical learningKey (lock)Statistical modelFeedforward neural networkIntersection (aeronautics)Machine learningPsychologyGeographyCommunicationCartographyComputer securityGaussian Processes and Bayesian InferenceTime Series Analysis and ForecastingAnomaly Detection Techniques and Applications