Generative models produce realistic objects in many domains, including text, image, video, and audio synthesis. Most popular models--Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)--usually employ a standard Gaussian distribution as a prior. Previous works show that the richer family of prior distributions may help to avoid the mode collapse problem in GANs and to improve the evidence lower bound in VAEs. We propose a new family of prior distributions--Tensor Ring Induced Prior (TRIP)--that packs an exponential number of Gaussians into a high-dimensional lattice with a relatively small number of parameters. We show that these priors improve Fréchet Inception Distance for GANs and Evidence Lower Bound for VAEs.

The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester. For example, consider building a telephony system that answers queries about your mortgage.

Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/Wi-Fi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS-/Wi-Fi-based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them.

Beginning in 2016, Microsoft rolled out a preview of Microsoft R Server (MRS) for Azure HDInsight clusters. Recent blog posts (by Max Kaznady and David Smith) have highlighted how to use and tune this service for large scale machine learning tasks. In this post, we push the envelope and show how to build an end-to-end fully operationalized analytics pipeline using Azure Data Factory (ADF) and MRS with HDInsight (specifically Apache Spark). By integrating Azure Data Factory with Microsoft R Server and Spark, we show how to configure a scalable training and testing pipeline that operates on large volumes of data.

Fb is aware of a heap about you thru the collected knowledge it has on every thing you Like, share and interact with on the platform, day-after-day. However the worth of that information is barely realized when Fb's capable of put it into context – by utilizing these insights to gas the Information Feed algorithm for instance, Fb can ship a way more custom-made and customized expertise, as a result of it is aware of what you're prone to be most all for. On this entrance, Fb's all the time trying to higher make the most of its information sources to customise and streamline your on-platform expertise. And their newest efforts on this entrance will just do that – although they could additionally make privateness advocates a bit of uneasy. In a brand new submit on Fb's Code weblog, the engineering crew have outlined what they're calling'DeepText'.