PPT-SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

Author : tatyana-admore | Published Date : 2018-03-15

Qingyuan Zhao 1 Murat A Erdogdu 1 Hera Y He 1 Anand Rajaraman 2 Jure Leskovec 2 Department of Statistics 1 and Computer Science 2 Stanford University Presenter

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "SEISMIC: A Self-Exciting Point Process M..." 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.

SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity: Transcript


Qingyuan Zhao 1 Murat A Erdogdu 1 Hera Y He 1 Anand Rajaraman 2 Jure Leskovec 2 Department of Statistics 1 and Computer Science 2 Stanford University Presenter Xueying Bai . Weina. . Ge. and Robert T. . Collins. CVPR 2009. .. Presented by,. Boddeti. . MohanVaraKrishna. M.E.(SSA). INTRODUCTION. A Bayesian marked point process (MPP) model is developed. to . detect and count people in crowded scenes. March 28, 2014. Association of Anesthesia Clinical Directors. Nashville, TN. Vikram . Tiwari, . Ph.D. .. . William R Furman, MD . Warren S Sandberg, MD, Ph.D.. Department . of . Anesthesiology, Vanderbilt University. Miles Osmaston. , Woking, UK.. Why do we need a new and versatile MOR model - THREE reasons . MOR structures change a lot with spreading rate (mm/yr). :-. Fast. (EPR 70-150) - straight axes, orthogonal segmentation, smooth flanks. Zhi. -Ming Ma. . ECM2013 . PolyU. . Email: mazm@amt.ac.cn . http://www.amt.ac.cn/member/mazhiming/index.html. A New Method for Coalescent Processes with Recombination . ?. Vernon Cormier, Lizzie Day, . Zack . Geballe, Marine . Lasbleis. , Mohammad . Youssof. , Han . Yue. Does the inner . core grows layer by . layer?. Does it . convect. ?. Deguen. , 2012. Modified . from a figure by . What we can learn from. what we can’t feel. Celeste Labedz. Massachusetts Institute of Technology. Incorporated Research Institutions for Seismology. Summer Internship 2014. What can ambient seismic. 3D seismic reflection image of the . Nankai. Subduction zone, Japan. Seismic Sources: Land Surveys. Many options! Hammer, weight-drop, dynamite, . vibroseis. . What frequencies are needed?. What energy level is needed (what distances do you need to cover)?. Amit . Suman and Tapan Mukerji. SCRF Annual Meeting. 8 - 9. th. May 2013. Stanford University. 2. Joint Inversion Loop. Generate. multiple. models. Evaluate. misfit. .. Reservoir. Model. Observed flow and seismic response. By . Amir Javed. Supervisor : Dr. Pete Burnap. Prof. Omer Rana. Problem. Identifies Trending Topics. #Trending topic . lmao this tweet by @user was nuts . Short_URL. User clicks on shortened URL. CS 5604 Information Storage and Retrieval. FALL 2017 . 12/15/17. Virginia Tech. Blacksburg, VA 24060. Team Members:. Ahmadreza Azizi. Deepika Mulchandani. Amit Naik. Khai Ngo . Suraj Patil. Arian Vezvaee. Group 5:. Katie Hardman. Tom . Horley. Daniel Hyatt. Executive Summary. Data Description. Data Preparation and Exploration. Scatter Plots of Grade and Finished Area vs Sale Price. Decision Tree Rules to predict highest and lowest Sale Prices. Earthquake Mechanisms. Brittle Mechanical Model: “stick-slip”. Focal point: 3D point inside the lithosphere where the seismic event occurs. Epicenter: projection of focal point to the map surface . Group 5:. Katie Hardman. Tom . Horley. Daniel Hyatt. Executive Summary. Data Description. Data Preparation and Exploration. Scatter Plots of Grade and Finished Area vs Sale Price. Decision Tree Rules to predict highest and lowest Sale Prices. Microblogging Platforms. Manish Gupta. 1. , Jing Gao. 2. , ChengXiang Zhai. 1. , Jiawei Han. 1. 1. UIUC . 2. SUNY. ASIST 2012. 6/24/2012. Abstract. We introduce . a novel . problem of .

Download Document

Here is the link to download the presentation.
"SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity"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.

Related Documents