Contests in Signal Processing and Machine Learning

 

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Contests in Signal Processing and Machine Learning

Recent years have seen a rapid increase in the number of machine learning and signal processing contests. Some of those currently running include:

From the domain of personalized medicine, there is a Kaggle contest focusing on automating personalized medicine for cancer treatment. The contest is accepted by the NIPS 2017 Competition Track. Currently the interpretation of genetic mutations present in cancer is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature. The goal of the contest is to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations.

Again from the domain of NIPS challenges, there is a series of competitions focusing on adversarial attacks, either targeted or non-targeted, as well as defense against them. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. To accelerate research on adversarial examples, Google Brain is organizing Competition on Adversarial Attacks and Defenses within the NIPS 2017 competition track. In the end of the competition the organizers will run all attacks against all defenses to evaluate how each of the attacks performs against each of the defenses.

From the domain of smart cities, there is another Kaggle competition, with the goal being to build a model that predicts the total ride duration of taxi trips in New York City. Your primary dataset is one released by the NYC Taxi and Limousine Commission, which includes pickup time, geo-coordinates, number of passengers, and several other variables. This contest promotes collaboration, therefore the organizers encourage participants (with cash prizes!) to publish additional training data that other participants can use for their predictions. They also have designated bi-weekly and final prizes to reward authors of kernels that are particularly insightful or valuable to the community.

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