PPT-Semi-Supervised Learning in Gigantic Image Collections

Rob Fergus New York University Yair Weiss Hebrew University Antonio Torralba MIT Presented by Gunnar Atli Sigurdsson TexPoint fonts used in EMF Read the TexPoint manual before you delete this box AAAAAAAAAA. CSCI-GA.2590 – Supplement for Lecture. 8. Ralph . Grishman. NYU. Flavors of learning. Supervised learning. All training data is labeled. Semi-supervised learning. Part of training data is labeled (‘the seed’). A Framework for Architectural Guidance Development. Mohsen . Anvaari. Norwegian University of Science and . Technology. Trondheim, . Norway. mohsena@idi.ntnu.no. . Olaf . Zimmermann. University of Applied Sciences of Eastern Switzerland . Ashwath Rajan. Overview, in brief. Marriage between statistics, linear algebra, calculus, and computer science. Machine Learning:. Supervised Learning. ex: linear Regression. Unsupervised Learning. ex: clustering. Jakob Verbeek. LEAR team, INRIA Rhône-Alpes. Outline of this talk. Motivation for “weakly supervised” learning. Learning MRFs for image region labeling from weak supervision. Models, Learning, Results. Yacine . Jernite. Text-as-Data series. September 17. 2015. What do we want from text?. Extract information. Link to other knowledge sources. Use knowledge (Wikipedia, . UpToDate,…). How do we answer those questions?. Philip . McParlane. , Yashar Moshfeghi and Joemon M. Jose. University of Glasgow, UK. http://www.dcs.gla.ac.uk/~philip/. p.mcparlane.1@research.gla.ac.uk. Motivation for annotating images. Problems with existing automatic image annotation collections. Classification. with Incomplete Class . Hierarchies. Bhavana Dalvi. ¶. *. , Aditya Mishra. †. , and William W. Cohen. *. ¶ . Allen Institute . for . Artificial Intelligence, . * . School Of Computer Science. CSCI-GA.2590. . Ralph . Grishman. NYU. Flavors of learning. Supervised learning. All training data is labeled. Semi-supervised learning. Part of training data is labeled (‘the seed’). Make use of redundancies to learn labels of additional data, then train model. Beth Parry. Common sense. Stubbornness. Luck. Background:. FIL conference 2012. Mainly Yes. No - short loan/ref. . - microfilm. - video/DVD. . - electronic (licence). . - Special Collections . Omer Levy. . Ido. Dagan. Bar-. Ilan. University. Israel. Steffen Remus Chris . Biemann. Technische. . Universität. Darmstadt. Germany. Lexical Inference. Lexical Inference: Task Definition. Dena B. French, . EdD. , RDN, . LD. ISPP Program Director & Experiential Coordinator. ISPP Class of 2017. Objectives. What is an ISPP?. Fontbonne’s. ISPP. Campus . “Tour”. Program overview & curriculum . Learning What is learning? What are the types of learning? Why aren’t robots using neural networks all the time? They are like the brain, right? Where does learning go in our operational architecture? Few-Shot Learning with Graph Neural Networks CS 330 Paper Presentation Problem Image source: Ravi, Sachin, and Hugo Larochelle. “Optimization as a model for few-shot learning,” 2017, 11. Some approaches to few-shot learning: 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..