TY  - CHAP
AU  - Sergio Escalera
AU  - Markus Weimer
AU  - Mikhail Burtsev
AU  - Valentin Malykh
AU  - Varvara Logacheva
AU  - Ryan Lowe
AU  - Iulian Vlad Serban
AU  - Yoshua Bengio
AU  - Alexander Rudnicky
AU  - Alan W. Black
AU  - Shrimai Prabhumoye
AU  - Łukasz Kidzinski
AU  - Mohanty Sharada
AU  - Carmichael Ong
AU  - Jennifer Hicks
AU  - Sergey Levine
AU  - Marcel Salathe
AU  - Scott Delp
AU  - Iker Huerga
AU  - Alexander Grigorenko
AU  - Leifur Thorbergsson
AU  - Anasuya Das
AU  - Kyla Nemitz
AU  - Jenna Sandker
AU  - Stephen King
AU  - Alexander S. Ecker
AU  - Leon A. Gatys
AU  - Matthias Bethge
AU  - Jordan Boyd Graber
AU  - Shi Feng
AU  - Pedro Rodriguez
AU  - Mohit Iyyer
AU  - He He
AU  - Hal Daume III
AU  - Sean McGregor
AU  - Amir Banifatemi
AU  - Alexey Kurakin
AU  - Ian Goodfellow
AU  - Samy Bengio
ED  - Sergio Escalera
ED  - Markus Weimer
PY  - 2018//
TI  - Introduction to NIPS 2017 Competition Track
BT  - The NIPS ’17 Competition: Building Intelligent Systems
SP  - 1
EP  - 23
PB  - Springer
N2  - Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter.
SN  - 978-3-319-94042-7
UR  - https://link.springer.com/chapter/10.1007%2F978-3-319-94042-7_1
N1  - HUPBA; no proj
ID  - Sergio Escalera2018
ER  -