PPT-Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling

Prince Wang William Wang UC Santa Barbara Outline VAE and the KL vanishing problem Motivation why Riemannian Normalizing flowWAE Details Experimental Results VAE KL vanishing. 1 A New Beginning 113 112 De nition of Bar Member 113 113 Variational Formulation 114 1131 The Total Potential Energy Functional 114 1132 Admissible Variations 116 1133 The Minimum Total Potential Energy Principle 116 1134 TPE Discretization 117 . Autoencoders. Theory and Extensions. Xiao Yang. Deep learning Journal Club. March 29. Variational. Inference. Use a simple distribution to approximate a complex distribution. Variational. parameter:. analysis and its applications on Riemannian manifolds. S. . Hosseini. FSDONA 2011, Germany.. Nonsmooth analysis. However, in many aspects of mathematics such as . control theory . and . matrix analysis, . CONTENTS. What is normalizing?. Aim of Normalizing. Objective of Normalizing. Normalizing. Cooling in Normalizing. Normalising of cold worked steel. Advantages. Limitations. What is Normalizing ?. NORMALIZING OF STEEL is a heat-treating process that is often considered from both thermal and microstructural standpoints. . Yahia. Saeed, . Jiwoong. Kim, Lewis Westfall, and Ning Yang. Seidenberg School of CSIS. Pace University, New York. Optical Character Recognition. Convert text into machine . processable. data. 1910s. Qifeng. Chen. Stanford University. Vladlen. . Koltun. Intel Labs. Optical flow. Motion field between two image frames. Optical flow. Motion field between two image frames. Image 1. Image 2. optical flow. Inference. Dave Moore, UC Berkeley. Advances in Approximate Bayesian Inference, NIPS 2016. Parameter Symmetries. . Model. Symmetry. Matrix factorization. Orthogonal. transforms. Variational. . a. Professor Ke-Sheng Cheng. Dept. of Bioenvironmental Systems Engineering. National Taiwan University. Runoff generating process. Rainfall losses during a storm event. Interception. Depression storage. Adding It All Up. Original Data. Equated Day . Factors. Holiday Factors. Normalized Data. Initial Seasonal Factors. Seasonally-Adjusted Data:. Initial. Seasonally-Adjusted Data:. Initial. Growth Rate. Predictively Modeling Social Text William W. Cohen Machine Learning Dept. and Language Technologies Institute School of Computer Science Carnegie Mellon University Joint work with : Amr Ahmed, Andrew Arnold, Ramnath Balasubramanyan, Frank Lin, Matt Hurst (MSFT), Ramesh Nallapati, Noah Smith, Eric Xing, Tae Yano Sagar. . Samtani. and . Hsinchun. Chen. Artificial Intelligence Lab, The University of Arizona. 1. Outline. Introduction and Background. Autoencoder. : Intuition and Formulation. Autoencoder. Variations: . Henning Lange, Mario . Bergés. , Zico Kolter. Variational Filtering. Statistical Inference. (Expectation Maximization, Variational Inference). Deep Learning. Dynamical Systems. Variational Filtering. NS Modeling - Current state. NS analysis . and. . modeling. . requires. . the. . transformation. of . requirements. . into. data, . tasks. . and. . flows. DATA. TASK. FLOW. NS Modeling - Current state. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments..