PPT-Efficient Large-Scale Structured Learning
Author : mitsue-stanley | Published Date : 2017-01-18
Steve Branson Oscar Beijbom Serge Belongie CVPR 2013 Portland Oregon UC San Diego UC San Diego Caltech Overview Structured prediction Learning from larger
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Efficient Large-Scale Structured Learning: Transcript
Steve Branson Oscar Beijbom Serge Belongie CVPR 2013 Portland Oregon UC San Diego UC San Diego Caltech Overview Structured prediction Learning from larger datasets. By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Raw Scale Raw Scale Raw Scale Raw Scale Score Score Score Score Score Score Score Score 86 100 64 80 42 66 20 42 85 98 63 79 41 66 19 41 84 97 62 79 40 65 18 39 83 95 61 78 39 64 17 38 82 94 60 77 38 . The Black Queen Hypothesis . (Morris et al. 2012):. All biological functions have a cost. If all things are equal, excluding a function causes a fitness advantage. Products of ‘leaky’ biological functions are unavoidably made available to the community, . Sanjeev. . Arora. , . Rong. . Ge. Princeton University. Learning Parities with Noise. Secret u = (1,0,1,1,1). u ∙ (0,1,0,1,1) = 0. u ∙ (1,1,1,0,1) = 1. u ∙ (0,1,1,1,0) = . 1. Learning Parities with Noise. Introductions . Name. Department/Program. If research, what are you working on.. Your favorite fruit.. How do you estimate P(. y|x. ) . Types of Learning. Supervised Learning. Unsupervised Learning. Semi-supervised Learning. Martin Burtscher. Department of Computer Science. High-End CPUs and GPUs. Xeon X7550 Tesla C2050. Cores 8 (superscalar) 448 (simple). Active threads 2 per core 48 per core. Frequency 2 GHz 1.15 GHz. Structured Training Program. The . DOTD . Structured Training Program . is a department-sanctioned, progressive training curriculum that requires specific work-related training be completed at each level of an employee’s career path. . Sanjeev. . Arora. , . Rong. . Ge. Princeton University. Learning Parities with Noise. Secret u = (1,0,1,1,1). u ∙ (0,1,0,1,1) = 0. u ∙ (1,1,1,0,1) = 1. u ∙ (0,1,1,1,0) = . 1. Learning Parities with Noise. Non-Volatile Main Memory. Qingda Hu*, . Jinglei Ren. , Anirudh Badam, and Thomas Moscibroda. Microsoft Research. *Tsinghua University. Non-volatile memory is coming…. Data storage. 2. Read: ~50ns. Loomis Union School District. PBIS Coaches Institute. January 20, 2015. Disclaimer: . This is a Discussion Session. What has worked . at one of our sites. ?. What are some of the benefits?. What are some of the challenges?. 1. Loops in C. C has three loop statements: the . while. , the . for. , and the . do…while. . The first two are pretest loops, and the. the third is a post-test loop. We can use all of them. for event-controlled and counter-controlled loops.. May 17. BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart. 1. Jinhong Jung. Namyong. Park. Lee . Sael. U Kang. Outline. Introduction. Proposed Method. Experiment. Conclusion. approaches. John Larmouth. ITU-T and ISO/IEC ASN.1 Rapporteur. j.larmouth@btinternet.com. Terminology has changed over time. Markup. languages. Abstract. Syntax and Concrete Syntax. Abstract syntax notation and encodings.
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