Multi-view Generative Adversarial Networks

International audienceLearning over multi-view data is a challenging problem with strong practical applications. Most related studies focus on the classification point of view and assume that all the views are available at any time. We consider an extension of this framework in two directions. First, based on the BiGAN model, the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from multi-view inputs. Second, it can deal with missing views and is able to update its prediction when additional views are provided. We illustrate these properties on a set of experiments over different datasets

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission

Triplet-based similarity score for fully multilabeled trees with poly-occurring labels

Motivation: The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies. Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases. Results: To overcome these limitations, in this article, we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data

The George-Anne

VALUES become GSU\u27s key focus this week Williams Center\u27s distinguishing feature disappears Students participate in governor\u27s debate New route results in mixed reactions VALUES in everyday life Fool me once, shame on you, but fool me again? Democrats have \u27no excuse\u27 for ending session early Letters to the editor Don\u27t be so quick to judge Police Beat Health Services offers free STD testing HPV vaccine clinics to be held on campus Southern Sprint for Wellness 5K Give blood, Win Plane Tickets! The Eagle Expo & Education Career Fair Dining for Success Program Statesboro City Council refuses to talk after resignation of city manager Think you\u27re depressed? Find out Thursday Fallin is back on the field The Buzz List Classifieds Lefty comes right back to the \u27Boro State of GSU volleybal

1952-02-14 Rowan County News

Rowan County News published on February 14, 1952

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

The BG News October 10, 1989

The BGSU campus student newspaper October 10, 1989. Volume 72 - Issue 30https://scholarworks.bgsu.edu/bg-news/5984/thumbnail.jp