This year marks the second annual “Competition Track” of NIPS. For the competition track there are a number of interesting events available to compete in. All the events are easy to join and span many areas of ML. The competitions are applicable to a wide variety of industries including automative, security, healthcare, and physics.

AutoML for Lifelong Machine Learning (Deadline October 26)

This competition focuses on building an algorithm which can automatically create predictive models for subsequent tasks without any human intervention. The competition is divided into two phases: the feedback phase and the test phase. In the feedback phase the organizers provide five datasets similiar in nature to the final test datasets. In the second phase the uploaded code of the models will be blindly evaluated on the five testsets. This competition is challenging as the distributions will slowly change over time along with the overall variety of feature types (i.e. categorical, temporal, binary, etc). All necessary data and instructions are availible on the official competiton website.

Adversarial Vision Challenge (Final Submission Date Nov 1)

This challenge focuses on making robust vision computer vision algorithms that are resilant to adversarial perturbations. This is important for defending computer vision models against malicious users and creating altogether more robust models. To get started you can fork the GitLab repository and the follow the directions.

The Conversational Intelligence Challenge 2 (ConvAI2) (September 30 cut off date for submisison)

This is a competition aimed at the development of non-goal oriented chatbots. For the competition they provide an interesting new dataset that was made by pairing crowd sourcing workers together, giving them a simple persona and having them chat. The evaluation will be done in three steps: through automated evaluation metrics (Perplexity, F1 and hits), evaluation on mechanical turk, and a “wild” evaluation where volunteers chat with the bots.

You can find source code for baseline models here. To get started you need to create a private Github repository and share it with the organizers. More information is available on the official website.

Tracking Machine Learning Challenge

This is a Physics related challenge that involves using machine learning to detect the path of particles following collisions. The competition is important due to the need to very quickly sort through particle collision data. Current, algorithms are expected not to scale for the increased amounts of data coming in from the LHC. The competition is ongoing and contains several different parts. The first phase which started back in May and will end in August focuses on accuracy of the reconstructed track of the particle. The second phase (the focus of the NIPs competition) focuses on speed of the model at test time.

PDF Slideshow on project motivation

Kaggle competition (completed)

Pommerman (deadline Nov 26)

The focus of this competition is to train a team of AI agents to compete against another players team of AI agents in the game of Pommerman, a variant of the strategy game of Bomberman. This competition is an intersting experiment in multi-agent learning in both the complementary and adverserial level. For more info see the Github or the official site link above.

InclusiveImage (Deadline Nov 9)

Another competition in the NIPs 2018 track . This competition aims to address problems with skewed data. “Concretely, in this competition researchers will train on Open Images [2], a large, multilabel, publicly-available image classification dataset that has been found to exhibit a geographical skew.”

AI Driving Olympics (Deadline Nov 30)

This one you may already be familiar with as it has been making its rounds around Reddit and other sites, but in case you are not I will describe it. The “AI Driving Olympics” officially starts in October and will consist of two parts the first at NIPS in December and the second at ICRA in May. The competition consists of three seperate tasks: lane following, lane following with dynamic vehicles, and finally navigation with dynamic obstacles. You can head to their official website for more information.

AI for Prosthetics Challenge

Last but certainly not least is the AI for prothetics challenge. This challenge is aimed at using RL to learn the optimal policy to help people with prosthetics to run. This challenge serves as a interesting application of RL to a real world problem. This is the second year that “learning to run” has been at NIPs and there are a fair number of articles to get you started. Finally, you can find out all the additional information and more on the GitHub repository for the challenge.

As you can see there is a real interesting crop of challenges this year at NIPs spanning a wide number of industries and areas of machine learning. Hopefully you find one that interests you and compete. Best of luck!