Introduction to NIPS 2017 Competition Track
- 620 Downloads
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.
The NIPS 2017 Competition track was sponsored by NIPS and ChaLearn.
The Conversational Intelligence Challenge was partially sponsored by Facebook, Flint Capital, IVADO, Microsoft Maluuba, Element AI.
The Learning to Run Challenge was organized by the Mobilize Center at Stanford University, a National Institutes of Health Big Data to Knowledge (BD2K) Center of Excellence supported through Grant U54EB020405, and by the crowdAI.org platform. The challenge was partially sponsored by NVIDIA, Amazon Web Services and Toyota Research Institute.
The Neural Art Challenge was sponsored by a number of sponsors, who we would like to thank. DeepArt.io sponsored the high-resolution renderings. ChaLearn.org sponsored the printing of the posters. Prices were sponsored by NVIDIA.
The IBM Watson AI XPRIZE is sponsored by IBM Watson.
- Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.Google Scholar
- Scott L Delp, Frank C Anderson, Allison S Arnold, Peter Loan, Ayman Habib, Chand T John, Eran Guendelman, and Darryl G Thelen. Opensim: open-source software to create and analyze dynamic simulations of movement. IEEE transactions on biomedical engineering, 54(11):1940–1950, 2007.CrossRefGoogle Scholar
- Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2414–2423, 2016.Google Scholar
- Łukasz Kidziński, Ong Carmichael Sharada, Mohanty, Jennifer Hicks, Sean Francis, Sergey Levine, Marcel Salathé, and Scott Delp. Learning to run challenge. In Sergio Escalera and Markus Weimer, editors, NIPS 2017 Competition Book. Springer, Springer, 2018.Google Scholar
- Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. 2015.Google Scholar