Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.

Data-driven model training is increasingly relying on finding Nash equilibria with provable techniques, e.g., for GANs and multi-agent RL. In this paper, we analyse a new extra-gradient method, that performs gradient extrapolations and updates on a random subset of players at each iteration. This approach provably exhibits the same rate of convergence as full extra-gradient in non-smooth convex games. We propose an additional variance reduction mechanism for this to hold for smooth convex games. Our approach makes extrapolation amenable to massive multiplayer settings, and brings empirical speed-ups, in particular when using cyclic sampling schemes. We demonstrate the efficiency of player sampling on large-scale non-smooth and non-strictly convex games. We show that the joint use of extrapolation and player sampling allows to train better GANs on CIFAR10.

Students hiring ghostwriters to write their assignments is an increasing problem in educational institutions all over the world, with companies selling these services as a product. In this work, we develop automatic techniques with special focus on detecting such ghostwriting in high school assignments. This is done by training deep neural networks on an unprecedented large amount of data supplied by the Danish company MaCom, which covers 90% of Danish high schools. We achieve an accuracy of 0.875 and a AUC score of 0.947 on an evenly split data set.

One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.

This is the third article of a three-part series of articles in Getting started Geographic Data Science with Python. You will learn about reading, manipulating and analysing Geographic data in Python. The third part, which is this article, covers a relevant and real-world project wrapping up to cement your learning. Learning Objectives for this case study are: 1. Apply spatial operations on real word dataset project 2. Spatial join and munging Geographic data. In this project, we will use two datasets: a population dataset disaggregated by age and preschools dataset from Statistics Sweden.

Artificial Intelligence in Transportation Market reports provides a comprehensive overview of the global market size and share. Artificial Intelligence in Transportation market data reports also provide a 5 year pre-historic and forecast for the sector and include data on socio-economic data of global. The Artificial Intelligence in Transportation market size will grow from USD XX Million in 2018 to USD XX Million by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2017, and the market size is projected from 2018 to 2023. Look insights of Global Artificial Intelligence in Transportation industry market research report at https://www.pioneerreports.com/report/361684

Dance is an art and when technology meets this kind of art, it's a novel attempt in itself. Several researchers have attempted to automate several aspects of dance, right from dance notation to choreography. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, etc. Despite several attempts by researchers for more than two decades in various styles of dance all round the world, we found a review paper that portrays the research status in this area dating to 1990 \cite{politis1990computers}. Hence, we decide to come up with a comprehensive review article that showcases several aspects of dance automation. This paper is an attempt to review research work reported in the literature, categorize and group all research work completed so far in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation namely dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.

With this summary, we can see important values such as R2, the F-statistic, and many others. You can also analyze a model using a graphical diagnostic such as plotting the residuals against the fitted/predicted values. Above is the fitted versus residual plot for our weight-height dataset, using height as the predictor. For the most part, this plot is random. However, as fitted values increase, so does the range of residuals.

Artificial Intelligence (AI) in Construction Market reports provides 5 year pre-historic and forecast for the sector and include data on socio-economic data of global. Key stakeholders can consider statistics, tables & figures mentioned in this report for strategic planning which lead to success of the organization.Artificial Intelligence (AI) in Construction Market reports provides a comprehensive overview of the global market size and share. Global Artificial Intelligence (AI) in Construction Market report provides strategists, marketers and senior management with the critical information they need to assess the global Artificial Intelligence (AI) in Construction sector. With the slowdown in world economic growth, the Keyword industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Keyword market size to maintain the average annual growth rate of XXX from XXX million $ in 2015 to XXX million $ in 2018, Industry Report analysts believe that in the next few years, Keyword market size will be further expanded, we expect that by 2024, The market size of the Keyword will reach XXX million $. The overviews, SWOT analysis and strategies of each vendor in the Artificial Intelligence (AI) in Construction market provide understanding about the market forces and how those can be exploited to create future opportunities.

Artificial intelligence has broken out of the black box and is now the not-so-secret recipe for real-world business results. Industry disrupters like Lyft, Uber, Airbnb, and LinkedIn are demonstrating the power of going all-in on machine learning solutions and AI tools. At the same time, both smaller and more traditional companies are seeing AI as a must-have capability. We want to take the temperature out there and learn exactly where companies, of all sizes, are at in their AI explorations, and the challenges and successes they're experiencing. That's why VB has launched a new AI survey for execs who may already be integrating AI into their workflows and product development, as well as those who are interested in learning how to get started, where to focus their resources, and what kind of results they can expect.