Hierarchical segmentation of graphical interfaces for Document Object Model reconstruction C˘at˘alin F. Pert, icas, 1 Mihai S. Baba 1 Homa Davoudi 1 R˘azvan V. Florian 1 Figure 1. Detection of graphical elements usin

Source URL: uclmr.github.io

• Computational neuroscience
• Artificial intelligence
• Artificial neural networks
• Computing
• Applied mathematics
• Computational statistics
• Market research
• Mathematical psychology
• Convolutional neural network
• Image segmentation
• Document Object Model
• Market segmentation

Inferring novel associations between SNP sets and gene sets in eQTL study using sparse graphical model Wei Cheng 1 , Xiang Zhang 2 , Yubao Wu 2 , Xiaolin Yin 2 , Jing Li 2 , David Heckerman 3 , and Wei Wang 4 1

Source URL: web.cs.ucla.edu

BUDVYTIS ET AL.: MOT FOR VIDEO SEGMENTATION 1 MoT - Mixture of Trees Probabilistic Graphical Model for Video Segmentation

Source URL: mi.eng.cam.ac.uk

DeepBach: a Steerable Model for Bach Chorales Generation Ga¨etan Hadjeres 1 2 Franc¸ois Pachet 1 2 Frank Nielsen 3 Abstract This paper introduces DeepBach, a graphical

Source URL: www.csl.sony.fr

COMPRESSIVE NONPARAMETRIC GRAPHICAL MODEL SELECTION FOR TIME SERIES Alexander Jung 1, Reinhard Heckel 2, Helmut B¨olcskei 2, and Franz Hlawatsch 1 1 Institute of Telecommunications, Vienna University of Technology, Aus

Source URL: www.reinhardheckel.com

  Real-time Blocks  Integrating Reactive Blocks with JamaicaVM  This collaborative concept paper highlights how graphical model-driven design, formal  analysis, and automated Java code generation can be combined wit

Source URL: www.bitreactive.com

Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan Tompson, Arjun Jain, Yann LeCun, Christoph Bregler New York University {tompson, ajain, yann, bregler}@cs.nyu.edu

Source URL: www.cims.nyu.edu

Graphical Model Sketch Branislav Kveton1 , Hung Bui2 , Mohammad Ghavamzadeh3 , Georgios Theocharous4 , S. Muthukrishnan5 , and Siqi Sun6 1 2 3

Source URL: www.bkveton.com

A HYBRID GRAPHICAL MODEL FOR ALIGNING POLYPHONIC AUDIO WITH MUSICAL SCORES Christopher Raphael School of Informatics Indiana University, Bloomington

Source URL: music.informatics.indiana.edu

In this talk, I will introduce hinge-loss Markov random fields (HLMRFs), a new kind of probabilistic graphical model that supports scalable collective inference from richly structured data. HL-MRFs unify three different

Source URL: mmds-data.org