PPT-Learning with Structured

Sparsity Authors Junzhou Huang Tong Zhang Dimitris Metaxas 1 Zhennan Yan Introduction Fixed set of p basis vectors where for each j gt Given a random observation which depends on an underlying coefficient vector . 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. Outline. Some Sample NLP Task . [Noah Smith]. Structured Prediction For NLP. Structured Prediction Methods. Conditional Random Fields. Structured . Perceptron. Discussion. Motivating Structured-Output Prediction for NLP. Simon . Lacoste-Julien. INRIA / . École Normale Supérieure. SIERRA Project Team. SMILE. . – November 4. th. 2013. Outline. Frank-Wolfe optimization. Frank-Wolfe for structured prediction. links with previous algorithms. Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . Purposeful Number Talks (part 2). Core Mathematics Partnership. Building Mathematical Knowledge and. High-Leverage Instruction for Student Success. Thursday . November 12. , . 2015. Learning Intention and Success Criteria. Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. 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. Qingda Hu*, . Jinglei Ren. , Anirudh Badam, and Thomas Moscibroda. Microsoft Research. *Tsinghua University. Non-volatile memory is coming…. Data storage. 2. Read: ~50ns. Write: ~10GB/s. Read: ~10µs. 1 . and p. 2. , what is the. Probability that you can click your way from p. 1. to p. 2. ?. <1%?, <10%?, >30%?. >50%?, ~100%? (answer at the end). CSE 494/598. . Information Retrieval, Mining and Integration on the Internet. Adherence to . Clinical . Guidelines . Emily Manlove, . MD. 1. ; . Tara Neil, . MD. 2. ; . Rachel . Griffith, DO. 2. ; . Mary . Masterman, MD. 2. ; Michelle Baalmann, MD. 2. ; Stephanie Shirey, MS2. 3. Teacher . Professional Development. Teacher Professional Development. In this . Teacher Professional Development. , you will find practical information on the following:. Overview of Structured Teaching .