![]() You are scored based on the distance you travel through the obstacle course in a set amount of time.Ĭoordinators: Carmichael Ong, Mohanty Sharada, Jason Fries, Jennifer HicksĪdvisors: Sergey Levine, Marcel Salathé, Scott Delp Potential obstacles include external obstacles like steps, or a slippery floor, along with internal obstacles like muscle weakness or motor noise. You are provided with a human musculoskeletal model and a physics-based simulation environment where you can synthesize physically and physiologically accurate motion. In this competition, you are tasked with developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible. Iker Huerga, Grigorenko, Das, Thorbergsson, Coordinators Participants will not only have an opportunity to work with real-world data and get to answer one of the key open questions in cancer genetics and precision medicine, but the winning model will be tested and deployed at Memorial Sloan Kettering and will have the potential to touch more than 120,000 patients it sees every year, and many more around the world. This competition is a challenge to develop classification models which analyze abstracts of medical articles and, based on their content accurately determine oncogenicity (4 classes) and mutation effect (9 classes) of the genes discussed in them. This dataset can be used to train machine learning models to help experts significantly speed up their research. It contains several thousand annotations of which genes are clinically actionable and which are not based on clinical literature. For the past several years, world-class researchers at Memorial Sloan Kettering Cancer Center have worked to create an expert-annotated precision oncology knowledge base. While the role of genetic testing in advancing our understanding of cancer and designing more precise and effective treatments holds much promise, progress has been slow due to significant amount of manual work still required to understand genomics. This competition requires the help of human evaluators! Black, Carnegie Mellon University, PittsburghĬall for human evaluators. Iulian Serban, Yoshua Bengio, University of Montreal, MontrealĪlexander Rudnicky, Alan W. Mikhail Burtsev, Valentin Malykh, MIPT, Moscow We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations of news articles with humans. ![]() This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. In addition to the growing real-world applications, the ability to converse is also closely related to the overall goal of AI. More details below! The Conversational Intelligence Challengeĭialogue systems and conversational agents – including chatbots, personal assistants and voice control interfaces – are becoming increasingly widespread in our daily lives. Top submissions will be invited to make a talk/poster at NIPS competition track. Human-Computer Question Answering Competition The Conversational Intelligence ChallengeĬlassifying Clinically Actionable Genetic MutationsĪpplied ML Day, Switzerland, travel grant Organizers and participants will be invited to submit their contribution as a book chapter to the upcoming NIPS 2017 Competition book, within Springer Series in Challenges in Machine Learning. Competition track day at the conference will be on December 8th. The results of the competitions, including organizers and top ranked participants talks will be presented during the Competition track day at NIPS 2017. Each competition has its own schedule defined by its organizers. Please visit each competition webpage to read more about the competition, its schedule, and how to participate. Below, you can find the five accepted competitions. Evaluation was based on the quality of data, problem interest and impact, promoting the design of new models, and a proper schedule and managing procedure. Five top-scored competitions were accepted to be run and present their results during the NIPS 2017 Competition track day. Proposals were reviewed by several high qualified researchers and experts in challenges organization. We received 23 competition proposals related to data-driven and live competitions on different aspects of NIPS. This is the first NIPS edition on "NIPS Competitions".
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