PPT-Constrained Semi-Supervised Learning

Author : karlyn-bohler | Published Date : 2015-10-22

using Attributes and Comparative Attributes Abhinav Shrivastava Saurabh Singh Abhinav Gupta The Robotics Institute Carnegie Mellon University Supervision Supervised

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Constrained Semi-Supervised Learning" is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Constrained Semi-Supervised Learning: Transcript

using Attributes and Comparative Attributes Abhinav Shrivastava Saurabh Singh Abhinav Gupta The Robotics Institute Carnegie Mellon University Supervision Supervised Active Learning BigData. using . Attributes and Comparative Attributes. Presenter : Ankit Laddha. Most of the slides are borrowed from . Abhinav. . Shrivastava’s. ECCV talk. Outline. Supervision based problem definitions. Low-Resource Languages. Dan . Garrette. , Jason . Mielens. , and Jason . Baldridge. Proceedings of ACL 2013. Semi-Supervised Training. HMM with Expectation-Maximization (EM). Need:. Large . raw. corpus. John Blitzer. 自然语言计算组. http://research.microsoft.com/asia/group/nlc/. Why should I know about machine learning? . This is an NLP summer school. Why should I care about machine learning?. Peter Divone Sr., P.E.. Director, Process Development. Global Skin Category R&D. Prepared for the . Integrated Continuous . Biomanufacturing. Conference. October 20-24, 2013. Castelidefeis. , Spain. 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?. (in tiny space). Giuseppe . Ottaviano. Roberto . Grossi. (. Università. di Pisa). {"timestamp": "2006-04-03 21:31:35", "user": "1578922", "query": ". londn. news"}. {". timestamp": "2006-04-08 14:09:27", "user": "18214495", "query": "craigslist. 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. The glue that helps hold sentences together. COLON-IZING Your Writing. A . colons are used . to introduce . more . information about something mentioned earlier in the sentence. . Shannon brought one thing: a stethoscope.. . 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. Omer Levy. . Ido. Dagan. Bar-. Ilan. University. Israel. Steffen Remus Chris . Biemann. Technische. . Universität. Darmstadt. Germany. Lexical Inference. Lexical Inference: Task Definition. 12019According to Family Code Section 3200 all providers of supervised visitation mustoperate their programs in compliance with the Uniform Standards of Practice for Providers of Supervised Visitation Follow. up - . months. Symptom. . Burden. Score. Abed . et al. ., JAMA 2013. AF symptom . severity. after . a supervised weight loss program and in a control group . Follow. up - . months. Symptom. 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..

Download Document

Here is the link to download the presentation.
"Constrained Semi-Supervised Learning"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.