PPT-A Three-way Model for Collective Learning on Multi-Relati

Author : luanne-stotts | Published Date : 2017-11-04

Author Maximilian Nickel Speaker Xinge Wen INTRODUCTION Multi relational Data Relational data is everywhere in our life WEB Social networks Bioinformatics INTRODUCTION

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A Three-way Model for Collective Learning on Multi-Relati: Transcript

Author Maximilian Nickel Speaker Xinge Wen INTRODUCTION Multi relational Data Relational data is everywhere in our life WEB Social networks Bioinformatics INTRODUCTION Why Tensor . Place these early hominids in chronological order. A. Homo sapiens. D. . . Australopithacus. E. . Homo erectus. C. Neanderthal. B. . Homo . habilis. Early Ancestors Image Sort. Answers:. A. Homo sapiens. A. KARTELJ and V. FILIPOVIC. School of Mathematics, University of Belgrade, Serbia. and. V. MILUTINOVIC. School of Electrical Engineering, University of Belgrade, Serbia. Agenda. Problem overview. Classification of the existing solutions. Tony Sang. , Concordia College. Dr. Derek A. Paley. , UMD . Collective Behavior. Autonomous agents within a group. Obey simple interaction rules. Lead to coherent group formation. Collective Behavior. Agent-based . Modeling. of . Industrial Eco-systems. Nigel Gilbert. Ozge. . Dilaver. . Kalkan. Jessica . Rowden. The Task. Building . a generic, multi-level model of industrial eco-systems (IE). . [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . heat exchangers. Modeling, optimization and heat integration. Candidate: Axel Holene ; . Supervisors: . Sigurd. . Skogestad. , Johannes . J. äschke. ;. Modeling. Mass and energy balances. Pinch concept. Judy Qiu, Indiana University. Prof. David Crandall. Computer Vision. Prof. Filippo Menczer. Complex Networks and Systems. Bingjing. Zhang. Acknowledgement. Xiaoming. . Gao. Stephen Wu. Thilina. . Volkan. Cevher. Laboratory. for Information and Inference Systems (LIONS). École. . Polytechnique. . Fédérale. de Lausanne (EPFL). Switzerland . http://lions.epfl.ch . . joint work with . Hemant. household. . bargaining. for . developing. countries. With. Roberta . Ziparo. , AMSE. Motivation. Issue: spouse interaction within households . poor . countries. Why does it matter? Why do we need a separate approach? . Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Navaneet Kumar, Sandeep Jain, Rajesh Jain. Networking & Multimedia Solutions Group. Updated Jan 2012. Outline. Virtual Platform – Challenges & Requirement. Significance of TLM 2.0. TLM LT Methodology. based. on . symmetry. and . geometry. (. with. a discussion of the . microscopic. drivers of . structural . evolution. ) . . R. F. . Casten. WNSL, Yale. Themes and challenges of Modern Science. Generative Adversarial Networks (GANs). Generative Adversarial Networks (GANs). Goodfellow. et al (2014) . https://arxiv.org/abs/1406.2661. Minimize distance between the distributions of real data and generated samples. Transfer Learning. Transfer a model trained on . source. data A to . target . data B. Task transfer: . in this case, . the source and target data can be the same. Image classification -> image segmentation.

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