PPT-On Unsupervised Feature Learning with

Deep Neural Networks Huan Sun Dept of Computer Science UCSB March 12 th 2012 Major Area Examination Committee Prof Xifeng Yan Prof Linda Petzold Prof Ambuj Singh. nyuedu httpwwwcsnyuedu yann Abstract We present an unsupervised method for learning a hier archy of sparse feature detectors that are invariant to smal shifts and distortions The resulting feature extractor co n sists of multiple convolution 64257lte 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 . Early Work. Why Deep Learning. Stacked Auto Encoders. Deep Belief Networks. CS 678 – Deep Learning. 1. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers). Temporal Commonality Discovery. Wen-Sheng . Chu. , . Feng. Zhou and Fernando De la Torre. Robotics Institute, Carnegie Mellon University. July 9, . 2013. 1. Unsupervised Commonality Discovery. in . Images. to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. . VATS lobectomy consultant mentoring. Leads: Tom Routledge, Mike Shackcloth. Background. UK VATS lobectomy uptake remains patchy. Increasing evidence that it is standard of care for early stage lung cancer. 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:. ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. ShaSha. . Xie. * Lei Chen. Microsoft ETS. 6/13/2013. Model Adaptation, Key to ASR Success. http://youtu.be/5FFRoYhTJQQ. Adaptation. Modern ASR systems are statistics-rich. Acoustic model (AM) uses GMM or DNN. Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections June 21 ACL 2011 Slav Petrov Google Research Dipanjan Das Carnegie Mellon University Part-of-Speech Tagging Portland has a thriving music scene . 20TimeConstraintsDa 200 18C 14 3PropertyG 6 10Hierarch 200 21PointFeatu 12Sheet 12Sheet 12FeatureE 71 Relate Relat Relat Data 20Sheet 9Featur 9Fea 9Featur 9Featur 9This Sheet12 91 9UnionoThis Sheet 9 The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Unsu. pervised . approaches . for . word sense disambiguation. Under the guidance of. Slides by. Arindam. . Chatterjee. &. Salil. Joshi. Prof. . Pushpak . Bhattacharyya. May 01, 2010. roadmap. Bird’s Eye View.. USDA Forest Service. Juliette Bateman (she/her). Remote Sensing Specialist/Trainer, . juliette.bateman@usda.gov. Soil Mapping and Classification in Google Earth Engine. Day 2:. Supervised and Unsupervised Classifications.