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The NIPS '17 Competition: Building Intelligent Systems

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Cover of 'The NIPS '17 Competition: Building Intelligent Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to NIPS 2017 Competition Track
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    Chapter 2 The First Conversational Intelligence Challenge
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    Chapter 3 ConvAI Dataset of Topic-Oriented Human-to-Chatbot Dialogues
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    Chapter 4 A Talker Ensemble: The University of Wroclaw’s Entry to the NIPS 2017 Conversational Intelligence Challenge
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    Chapter 5 Multi-view Ensemble Classification for Clinically Actionable Genetic Mutations
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    Chapter 6 Learning to Run Challenge: Synthesizing Physiologically Accurate Motion Using Deep Reinforcement Learning
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    Chapter 7 Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments
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    Chapter 8 Reinforcement Learning to Run… Fast
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    Chapter 9 Human-Computer Question Answering: The Case for Quizbowl
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    Chapter 10 Studio Ousia’s Quiz Bowl Question Answering System
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    Chapter 11 Adversarial Attacks and Defences Competition
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    Chapter 12 First Year Results from the IBM Watson AI XPRIZE: Lessons for the “AI for Good” Movement
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    Chapter 13 Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health
Attention for Chapter 9: Human-Computer Question Answering: The Case for Quizbowl
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Citations

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Summary Dimensions citations
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Chapter title
Human-Computer Question Answering: The Case for Quizbowl
Chapter number 9
Book title
The NIPS '17 Competition: Building Intelligent Systems
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-319-94042-7_9
Book ISBNs
978-3-31-994041-0, 978-3-31-994042-7
Authors

Jordan Boyd-Graber, Shi Feng, Pedro Rodriguez, Boyd-Graber, Jordan, Feng, Shi, Rodriguez, Pedro

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 2 50%
Student > Bachelor 1 25%
Student > Master 1 25%
Readers by discipline Count As %
Computer Science 3 75%
Unknown 1 25%

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