PPT-A Hierarchical Deep Temporal Model for

Group Activity Recognition MSc Thesis Defence Srikanth Muralidharan 12 April 2016 Outline Part I Introduction to Group Activity Part II Description of the Model Part III Experimental Results and Conclusion . Tugba . Koc Emrah Cem Oznur Ozkasap. Department of . Computer . Engineering, . Koç . University. , Rumeli . Feneri Yolu, Sariyer, Istanbul . 34450 Turkey. Introduction. Epidemic (gossip-based) principles: highly popular in large scale distributed systems. Large Scale Visual Recognition Challenge (ILSVRC) 2013:. Detection spotlights. Toronto A team. Latent Hierarchical Model with GPU Inference for Object Detection. Yukun Zhu, Jun Zhu, Alan Yuille . UCLA Computer Vision Lab. Liu . ze. . yuan. May 15,2011. What purpose does . Markov Chain Monte-Carlo(MCMC) . serve in this chapter?. Quiz of the Chapter. 1 Introduction. 1.1Keywords. 1.2 Examples. 1.3 Structure discovery problem. ISHAY BE’ERY. ELAD KNOLL. OUTLINES. . Motivation. Model . c. ompression: mimicking large networks:. FITNETS : HINTS FOR THIN DEEP NETS . (A. Romero, 2014). DO DEEP NETS REALLY NEED TO BE DEEP . (Rich Caruana & Lei Jimmy Ba 2014). Data . Models. Fabio . Grandi. fabio.grandi@unibo.it. DISI, . Università di Bologna. A short course on Temporal . Databaes. for DISI PhD students, 2016 . Credits: most of the materials used is taken from slides prepared by Prof. M. . Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. 2016. RECAP. trend of mother to child HIV transmission in . western . Kenya, . 2007-2013. Anthony Waruru. , Thomas Achia, . Hellen . Muttai, . Lucy . Ng’ang’a, . Abraham . Katana, . Peter . Young, . Jim . Tobias, Peter Juma, . Li Deng . Deep Learning Technology Center. Microsoft AI and Research Group. Invited Presentation at NIPS Symposium, December 8, 2016. Outline. Topic one. : RNN versus Nonlinear Dynamic Systems;. sequential discriminative vs. generative models. Laya. . Shamgah. Advisor: Dr. . Karimoddini.  .  .  . TECHLAV Project. Testing, Evaluation and Control of Heterogeneous Large-scale Autonomous systems of Vehicles (TECHLAV). ​. Thrust 1: Modeling, Analysis and Control of Large-scale Autonomous Vehicles (MACLAV). Sandhu. CORE RBAC. HIERARCHICAL RBAC. SSD IN . HIERARCHICAL RBAC. DSD IN . HIERARCHICAL RBAC. NIST MODEL FAMILY. COMPARE RBAC96. RBAC0. BASIC RBAC. RBAC3. ROLE HIERARCHIES . CONSTRAINTS. RBAC1. ROLE. Deep Learning for CT Scan Identification of Temporal Bone and Skull Base Landmarks. The temporal bone and skull base are complex areas that have multiple nerves, arteries, veins and other important structures encased in bone. Outline. What is Deep Learning. Tensors: Data Structures for Deep Learning. Multilayer Perceptron. Activation Functions for Deep Learning. Model Training in Deep Learning. Regularization for Deep Learning. Hierarchical Temporal Memory (and LSTM). Jaime Coello de Portugal. Many thanks to . Jochem. . Snuverink. Motivation. Global outlier. Level change. Pattern deviation. Pattern change. Plots from: Ted . Raghu Machiraju. Firdaus. . Janoos. , Fellow, Harvard Medical. Istavan. (. Pisti. ) . Morocz. , . Instuctor. , Harvard . Medical. Premise. Understanding the mind not only requires a comprehension of the workings of low–level neural networks but also demands a detailed map of the brain’s functional architecture and a description of the large–scale connections between populations of neurons and insights into how relations between these simpler networks give rise to higher–level thought.