PPT-Large-Scale Factorization of Type-Constrained Multi-Relational Data

Author : morgan | Published Date : 2023-09-19

Denis Krompaß 1 Maximilian Nickel 2 and Volker Tresp 13 1 Department of Computer Science Ludwig Maximilian University 2 MIT Cambridge and Istituto Italiano

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Large-Scale Factorization of Type-Constrained Multi-Relational Data: Transcript

Denis Krompaß 1 Maximilian Nickel 2 and Volker Tresp 13 1 Department of Computer Science Ludwig Maximilian University 2 MIT Cambridge and Istituto Italiano di Tecnologia. Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. David Wallom . Overview. The problem…. Other communities. The pace of technological change. Using the data. The problem…. New telescopes generate vast amounts of data. Particularly (but not limited to) surveys (SDSS, PAN-STARRS, LOFAR, SKA…). Data Analysis on . MapReduce. Chao Liu, Hung-. chih. Yang, Jinliang Fan, Li-Wei He, Yi-Min Wang. Internet Services Research Center (ISRC). Microsoft Research Redmond. Internet Services Research Center (ISRC). Department of Electrical and Computer Engineering. Lanchao. Liu and Zhu Han. Department. of Electrical and Computer Engineering. Department of . Computer Science. University of Houston. Supported by . Kamini. Yadav. Dr. Russ . Congalton. Current Process Flow Chart. Testing Protocol on Mali data. Evaluate Mali data collected in August 2015 by . Murali. , according to the flowchart made by Justin. Large Scale. Kuan-Chuan. Peng. Tsuhan. Chen. 1. Introduction. Breakthrough progress in object classification.. 2. O. . Russakovsky. . et al. . ImageNet. . large scale visual recognition challenge. .. . arXiv:1409.0575, 2014.. Corrinne Yu. Halo team Principal engine programmer. Corrinne.Yu@microsoft.com. Zen of multi core rendering. Take away. Compilation and survey of effective rendering techniques for current generation multi core console hardware . under Additional Constraints. Kaushik . Mitra. . University . of Maryland, College Park, MD . 20742. Sameer . Sheorey. y. Toyota Technological Institute, . Chicago. Rama . Chellappa. University of Maryland, College Park, MD 20742. Zhenhong. Chen, . Yanyan. . Lan. , . Jiafeng. . Guo. , Jun . Xu. , and . Xueqi. Cheng . CAS Key Laboratory of Network Data Science and Technology,. Institute . of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Analysis. . . Kai-Wei Chang. Joint work with. . Scott Wen-tau . Yih, Chris Meek. Microsoft Research. Natural Language Understanding. Build an intelligent system that can interact with human using natural language. Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Analysis. . . Kai-Wei Chang. Joint work with. . Scott Wen-tau . Yih, Chris Meek. Microsoft Research. Natural Language Understanding. Build an intelligent system that can interact with human using natural language. Section 1. Tutorial on Learning Bayesian Networks for Relational Data. Overview. What are relational data?. Different notations/representations.. Logic. Tables. Graph. RDF. Matrix/Tensor. Common core: .

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