PPT-Hetero-Labeled LDA: A partially supervised topic model with heterogeneous label information

Dongyeop Kang 1 Youngja Park 2 Suresh Chari 2 1 IT Convergence Laboratory KAIST InstituteKorea 2 IBM TJ Watson Research Center NY USA. Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . Learning. An example. From . Xu. et al., “Training . SpamAssassin. with Active Semi-Supervised Learning”. Semi-Supervised and Active Learning . Semi-Supervised learning: . Using a combination of labeled and unlabeled examples, or using partially labeled examples. Introductions . Name. Department/Program. If research, what are you working on.. Your favorite fruit.. How do you estimate P(. y|x. ) . Types of Learning. Supervised Learning. Unsupervised Learning. Semi-supervised Learning. Aaron M. Schinder, . Prof. Mitchell . Walker, . Prof. Julian . Rimoli. High-Power . Electric Propulsion Lab. Georgia Institute of Technology . 49th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit . Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. 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?. Yizhou. Sun, Rick Barber, Manish Gupta, . Charu. . C. . Aggarwal. , . Jiawei. Han. 1. Content. Background and motivation. Problem definition. PathPredict. : meta path-based . relationship prediction . David Kauchak. CS 451 – Fall 2013. Why are you here?. What is Machine Learning?. Why are you taking this course?. What topics would you like to see covered?. Machine Learning is…. Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.. Classification. with Incomplete Class . Hierarchies. Bhavana Dalvi. ¶. *. , Aditya Mishra. †. , and William W. Cohen. *. ¶ . Allen Institute . for . Artificial Intelligence, . * . School Of Computer Science. . 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. 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: In 2D DIGE, protein samples are labeled with size and chargematched CyDyefluorsThree fluorescently labeled protein samples can be combined and resolved using 2D gel electrophoresis: isoelectric focus . on Microblogs. Hongzhao Huang. huangh9@rpi.edu. Advisor: Dr. Heng Ji. Computer Science Department. Rensselaer Polytechnic Institute. April 9, 2015. Doctoral Committee: . Dr. Heng Ji (. Chair, RPI). Self-Learning Learning . Technique. . for. Image . Disease. . Localization. . Rushikesh. Chopade1, . Aditya. Stanam2, . Abhijeet. Patil3 & . Shrikant. Pawar4*. 1. Department of . Geology.