invited talk abstract
Invited Talk Abstracts
Eberle, William (Tennessee Technological University) | Boonthum-Denecke, Chutima (Hampton University)
Abstracts of the invited talks presented a the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference (FLAIRS-27) held May 21-23, 2014, in Pensacola, Florida USA.
Invited Talk Abstracts
Howard, Ayanna (Georgia Institute of Technology) | Johnson, David (Decooda) | Conati, Cristina (University of British Columbia) | Chen, Frederick W. (Signal Systems Corporation) | Bjorner, Nikolaj (Microsoft Research)
Abstracts of the invited talks presented at the 2013 FLAIRS conference. Talks include Robotics and Assistive Technologies: Their Emerging Role in Healthcare (Howard); Crossing the Data Science Chasm: The Perception of What Data Science Is and What It Needs to Be (Johnson); Who Are My Users and How I Can Help Them? The Quest of User-Adaptive Interaction (Conati); Neural Networks in Satellite-Based Atmospheric Remote Sensing (Chen); and The Use of Automated Reasoning for Software Verification of Microsoft Products (Bjorner).
Invited Talk Abstracts
Landauer, Thomas K. (Pearson Knowledge Technologies) | Picard, Rosalind W. (Massachusetts Institute of Technology) | Touretzky, David S. (Carnegie Mellon University) | Baker, Ryan (Worcester Ploytechnic Institute) | Holte, Robert C. (University of Alberta) | Stent, Amanda J. (AT&T Labs - Research) | Vanderveken, Daniel (University of Quebec)
Thomas K. Landauer (Pearson Knowledge Technologies) The recently created word maturity (WM) metric uses the computational language model LSA to mimic the average evolutionary growth of individual word and paragraph knowledge as a function of the total amount and order of simulated reading. The simulator traces the separate growth trajectories of an unlimited number of different words from the beginning of reading to adult level.
Invited Talk Abstracts
Ma, Yi (University of Illinois at Urbana-Champaign) | Sha, Fei (University of Southern California) | Carin, Lawrence (Duke University) | Lerman, Gilad (University of Minnesota) | Lawrence, Neil (University of Manchester)
Both Lawrence Carin tools utilize the same transformed Robust PCA model for the visual data: D A E, and use practically the same Hierarchical Bayesian methods are employed to learn a reversible algorithm for extracting the low-rank structures A from the statistical embedding. The proposed embedding visual data D, despite image domain transformation T and procedure is connected to spectral embedding methods (e.g., corruptions E. We will show how these two seemingly simple diffusion maps and Isomap), yielding a new statistical spectral tools can help unleash tremendous information in images framework. The proposed approach allows one to discard and videos that we used to struggle to get. We believe these the training data when embedding new data, allows synthesis new tools will bring disruptive changes to many challenging of high-dimensional data from the embedding space, tasks in computer vision and image processing, including and provides accurate estimation of the latent-space dimensionality.