PDF-Learning with Marginalized Corrupted Features

InsummarywemakethefollowingcontributionsiweintroducelearningwithmarginalizedcorruptedfeaturesMCFaframeworkthatregularizesclassiersbymarginalizingoutfeaturecorruptionsiiwederiveanalyticalsolu. Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . learning and prediction. Jongmin. Kim. Seoul National University. Problem statement. Predicting outcome of surgery. Predicting outcome of surgery. Ideal approach. . . . .. ?. Training Data. Predicting outcome. Serving the . Marginalized. “Do not mistreat an alien or oppress him, for you were aliens in Egypt. “Do not take advantage of a widow or an orphan. If you do and they cry out to me, I will certainly hear their cry. . Gibbs Models. Ce Liu. celiu@microsoft.com. How to Describe the Virtual World. Histogram. Histogram: marginal distribution of image variances. Non Gaussian distributed. Texture Synthesis (Heeger et al, 95). Insummary,wemakethefollowingcontributions:i)weintroducelearningwithmarginalizedcorruptedfea-tures(MCF),aframeworkthatregularizesclassiersbymarginalizingoutfeaturecorruptions;ii)wederiveanalyticalsolu REPAIRING A " CORRUPTED "WINDOWS.." HIGH - LEVEL EXECUTIVE SUMMARY : A "corrupted recycle bin" is an annoying hard drive error that makes the "Windows.." recycle bin incapable of storing "deleted" f via Brain simulations . Andrew . Ng. Stanford University. Adam Coates Quoc Le Honglak Lee Andrew Saxe Andrew Maas Chris Manning Jiquan Ngiam Richard Socher Will Zou . Thanks to:. Group 3. Marginalized population. Who are the marginalized population?. Poor. . p. opulation – . peri. -urban, slum dwellers illegal settlement. Rural population not having access to treatment. Women living with HIV. in Computer Vision. Adam Coates. Honglak. Lee. Rajat. . Raina. Andrew Y. Ng. Stanford University. Computer Vision is Hard. Introduction. One reason for difficulty: small datasets.. Common Dataset Sizes. in Computer Vision. Adam Coates. Honglak. Lee. Rajat. . Raina. Andrew Y. Ng. Stanford University. Computer Vision is Hard. Introduction. One reason for difficulty: small datasets.. Common Dataset Sizes. Authors: Jonathan Krause, . Timnit. . Gebru. , . Jia. Deng , Li-. Jia. Li, Li . Fei-Fei. ICPR, 2014. Presented by: Paritosh. 1. Problem addressed. Authors address the problem of Fine-Grained Recognition.  . STUDY AND ANALYSIS OF PERIODICNOISE. REDUCTION ALGORITHM IN FOURIER DOMAIN.  .  .  . By.  . Omar . Ibrahim . Abdoh. . Ahmed. . 431804773. . Fahad. Saud Hassan Al-. Zaidi. 431807167. Mosa. CS771: Introduction to Machine Learning. Nisheeth Srivastava. Plan for today. 2. Types of ML problems. Typical workflow of ML problems. Various perspectives of ML problems. Data and Features. Some basic operations of data and .