Truveo Video Search: Tachtalk Videos

New generation of math software from Maplesoft

Sat, 22 Sep 2007 08:13:38 -0400

Google Tech Talks September, 11 2007 ABSTRACT The name Maple is synonymous with doing complex math on computers. Best known for its symbolic or algebraic computation abilities, Maple is one of the most important tools for the modern applied mathematician and scientist. Many of you are likely familiar with Maple from college but you've probably not kept up to date with latest developments. This presentation will present some of the latest product developments from Maplesoft. Topics include - developments in high performance numerical computation - recent advances in symbolic computing - new Maple libraries including graph theory, statistics, optimization, polynomial operations, and more - parallel and grid computing - knowledge capture for mathematical documents - the Maple programming language and application development - overview of new add-on products including global optimization, and modeling and simulation The presenter will be Mohamed Bendame, a senior engineer from Maplesoft. The presentations will include an open Q session. This talk will be taped by the engEDU Tech Talks Team. Speaker: Mohamed Bendame

Domain Adaptation with Structural Correspondence Learning

Sun, 30 Sep 2007 03:16:25 -0400

Google Tech Talks September, 5 2007 ABSTRACT Statistical language processing tools are being applied to an ever-wider and more varied range of linguistic data. Researchers and engineers are using statistical models to organize and understand financial news, legal documents, biomedical abstracts, and weblog entries, among many other domains. Because language varies so widely, collecting and curating training sets for each different domain is prohibitively expensive. At the same time, differences in vocabulary and writing style across domains can cause state-of-the-art supervised models to dramatically increase in error. This talk describes structural correspondence learning (SCL), a method for adapting models from resource-rich source domains to resource-poor target domains. SCL uses unlabeled data from both domains to induce a common feature representation for domain adaptation. We demonstrate SCL for two NLP tasks: sentiment classification and part of speech tagging. For each of these tasks, SCL significantly reduces the error of a state-of-the-art discriminative model. Speaker: John Blitzer