PPT-Generative and Discriminative Voxel Modeling

Andrew Brock Introduction Choice of representation is key Background VoxNet Maturana et al 2015 Background VAEs Background VAEs VAE Architecture Reconstruction Objective Standard Binary CrossEntropy. Tom M Mitchell All rights reserved DRAFT OF January 19 2010 PLEASE DO NOT DISTRIBUTE WITHOUT AUTHORS PERMISSION This is a rough draft chapter intended for inclusion in a possible second edition of the textbook Machine Learn ing TM Mitchell McGraw H Given a new you want to predict its class The generative iid approach to this problem posits a model family xc c 1 and chooses the best parameters 955 by maximizing or integrating over the joint distribution where denotes the data D c 2 An Morphometry. Methods for Dummies 2013. Elin. Rees & Peter . McColgan. Contents. General idea. Pre-Processing. Spatial normalisation. Segmentation. Modulation. Smoothing. Statistical analysis. GLM. Agenda. Beyond Fixed . Keypoints. Beyond . Keypoints. Open discussion. Part Discovery from Partial Correspondence. [. Subhransu. . Maji. and Gregory . Shakhnarovich. , CVPR 2013]. K. eypoints. in diverse categories. . Justin . Chumbley. Laboratory for Social and Neural Systems Research. Institute for Empirical Research in Economics. University of Zurich. . With many thanks for slides & images to:. TNU/FIL . -Based . Morphometry. John . Ashburner. Wellcome. Trust Centre for . Neuroimaging. ,. 12 Queen Square, London, UK.. Overview. Voxel. -Based . Morphometry. Morphometry. in general. Volumetrics. VBM . etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. Yang Mu, Wei Ding. University of Massachusetts . Boston. 2013 IEEE International Conference on Data . Mining. , Dallas, . Texas, Dec. 7. PhD Forum. Classification. Distance learning. Feature selection. Logistic Regression, SVMs. CISC 5800. Professor Daniel Leeds. Maximum A Posteriori: a quick review. Likelihood:. Prior: . Posterior Likelihood x prior = . MAP estimate:. . .  . Choose . and . to give the prior belief of Heads bias . Kevin Tang. Conditional Random Field Definition. CRFs are a. . discriminative probabilistic graphical model . for the purpose of predicting sequence labels. . Models a . conditional. distribution . Chumbley. Laboratory for Social and Neural Systems Research. Institute for Empirical Research in Economics. University of Zurich. . With many thanks for slides & images to:. FIL Methods group. Overview of SPM. MfD. - 2017. What this talk covers. Preprocessing. in fMRI : Why is it needed?. Motion in fMRI. Realignment. Unwarping. How this all works in SPM. Scanner Output. Statistical analysis. Preprocessing. Generative vs. Discriminative models. Christopher Manning. Introduction. So far we’ve looked at “generative models”. Language models, Naive Bayes. But there is now much use of conditional or discriminative probabilistic models in NLP, Speech, IR (and ML generally). An Overview. Yidong. Chai. 1,2. , . Weifeng Li. 1,3. , Hsinchun Chen. 1. 1 . Artificial Intelligence Laboratory, The University of Arizona. 2 . Tsinghua University. 3 . University of Georgia. 1. Acknowledgements.