Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Spartan Daily, February 5, 2019

Volume 152, Issue 5https://scholarworks.sjsu.edu/spartan_daily_2019/1004/thumbnail.jp

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Easterner, Vol. 64, No. 25, May 1, 2013

This issue of the Easterner contains articles about a student art show, the upcoming Macklemore concert, increased turnout in Associated Students (ASEWU) elections, victim advocate Ginger Johnson, interview fashions, a campus talk by marriage equality activist Zach Walls, Cinco de Mayo activities on campus, the golf season, the club volleyball team, the football spring scrimmage, hammer thrower Jordan Arakawa, men\u27s rugby, and football player Brandon Kaufman.https://dc.ewu.edu/student_newspapers/1806/thumbnail.jp

Spartan Daily April 6, 2010

Volume 134, Issue 32https://scholarworks.sjsu.edu/spartandaily/1247/thumbnail.jp

The BG News August 26, 2008

The BGSU campus student newspaper August 26, 2008. Volume 99 - Issue 3https://scholarworks.bgsu.edu/bg-news/8942/thumbnail.jp

The Daily Texan

Texas Student Medi

Daily Eastern News: August 21, 2007

https://thekeep.eiu.edu/den_2007_aug/1001/thumbnail.jp