The PhysioNet/Computing in Cardiology Challenge 2017

1 февраля 2017 — 1 сентября 2017
Осталось 1 неделя, 1 день
The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an alternative rhythm, or is too noisy to be classified.

crowdAI: OpenSNP Height Prediction

3 июля 2017 — 1 сентября 2017
Осталось 1 неделя, 1 день
This challenge aims at predicting height based on genetics (DNA variation).

Visual Domain Adaptation Challenge

19 июня 2017 — 8 сентября 2017
Осталось 2 недели, 1 день
We are pleased to announce the 2017 Visual Domain Adaptation (VisDA2017) Challenge! It is well known that the success of machine learning methods on visual recognition tasks is highly dependent on access to large labeled datasets. Unfortunately, performance often drops significantly when the model is presented with data from a new deployment domain which it did not see in training, a problem known as dataset shift. The VisDA challenge aims to test domain adaptation methods’ ability to transfer source knowledge and adapt it to novel target domains.

Kaggle: Web Traffic Time Series Forecasting

13 июля 2017 — 10 сентября 2017
Осталось 2 недели, 3 дня
Sequential or temporal observations emerge in many key real-world problems, ranging from biological data, financial markets, weather forecasting, to audio and video processing. The field of time series encapsulates many different problems, ranging from analysis and inference to classification and forecast. What can you do to help predict future views?

Open Academic Data Challenge 2017

18 июля 2017 — 15 сентября 2017
Осталось 3 недели, 1 день
Based on the datasets provided by AMiner.org, a renowned academic data mining system and Microsoft Academic Graph, the participants are required to extract scholars’ personal description, analyze their research interests and predict the citation counts of their papers, so as to better provide the information of related experts, assess their research results, monitor certain scientific research progress and present academic development trends for the academic circles.

Kagge New York City Taxi Trip Duration

20 июля 2017 — 15 сентября 2017
Осталось 3 недели, 1 день
In this competition, Kaggle is challenging you 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.

Large-Scale Video Classification Challenge

23 июля 2017 — 18 сентября 2017
Осталось 3 недели, 4 дня
This newly collected dataset contains over 8000 hours of video data from YouTube and Flicker, annotated into 500 categories. The categories cover a wide range of popular topics like social events (e.g., “tailgate party”), procedural events (e.g., “making cake”), objects (e.g., “panda”), scenes (e.g., “beach”), etc. Compared with FCVID, new categories are added to enrich the original hierarchy. For example, 76 new categories are added to "cooking" totaling 93 classes, and 75 new classes are added to "sports". During annotation, multiple labels have been considered as much as possible for each video. When labeling a particular category, categories that are not likely to co-occur are filtered out manually with the remaining labels considered for annotation.

NIPS 2017: Learning to Run

20 июня 2017 — 20 сентября 2017
Осталось 3 недели, 6 дней
Your task is to build a function f which takes the current state observation (a 41 dimensional vector) and returns the muscle excitations action (18 dimensional vector) in a way that maximizes the reward. Your total reward is the position of the pelvis on the x axis after the last iteration minus a penalty for using ligament forces. Ligaments are tissues which prevent your joints from bending too much - overusing these tissues leads to injuries, so we want to avoid it. The penalty in the total reward is equal to the sum of forces generated by ligaments over the trial, divided by 1000. For details on evaluation please refer to the Getting Started guide in the Dataset section of the challenge.

Kaggle: Carvana Image Masking Challenge

26 июля 2017 — 28 сентября 2017
Осталось 1 месяц
In this competition, you’re challenged to develop an algorithm that automatically removes the photo studio background. This will allow Carvana to superimpose cars on a variety of backgrounds. You’ll be analyzing a dataset of photos, covering different vehicles with a wide variety of year, make, and model combinations.

Kaggle: NIPS 2017 Defense Against Adversarial Attack

4 июля 2017 — 1 октября 2017
Осталось 1 месяц, 1 неделя
Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. 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 Examples and Defenses within the NIPS 2017 competition track.

Kaggle: Personalized Medicine Redefining Cancer Treatment

26 июня 2017 — 1 октября 2017
Осталось 1 месяц, 1 неделя
For this competition MSKCC is making available an expert-annotated knowledge base where world-class researchers and oncologists have manually annotated thousands of mutations. We need your help to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations.

Kaggle: NIPS 2017 Non-targeted Adversarial Attack

4 июля 2017 — 1 октября 2017
Осталось 1 месяц, 1 неделя
This research competition doesn't follow Kaggle's normal submission process. See the Submission Format tab for more details. Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. 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 Examples and Defenses within the NIPS 2017 competition track.

Kaggle NIPS 2017 Targeted Adversarial Attack

4 июля 2017 — 1 октября 2017
Осталось 1 месяц, 1 неделя
Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. 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 Examples and Defenses within the NIPS 2017 competition track.

NEXAR: Vehicle Detection in the Wild using the NEXET Dataset

21 июля 2017 — 2 октября 2017
Осталось 1 месяц, 1 неделя
NEXET, the Nexar dataset, is a massive set consisting of 50,000 images from all over the world with bounding box annotations of the rear of vehicles collected from a variety of locations, lighting, and weather conditions. We are releasing this dataset to you, our challengers, to empower you to build a truly smart collision prevention system that can work extremely well anywhere and at any time.

NIPS 2017: The Conversational Intelligence Challenge

4 апреля 2017 — 12 ноября 2017
Осталось 2 месяца, 2 недели
Dialogue systems and conversational agents – including chatbots, personal assistants and voice control interfaces – are becoming increasingly widespread in our daily lives. In addition to the growing real-world applications, the ability to converse is also closely related to the overall goal of AI. Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations of news articles with humans. 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. 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.

The Alzheimer's Disease Prediction Of Longitudinal Evolution Challenge

15 июня 2017 — 15 ноября 2017
Осталось 2 месяца, 3 недели
TADPOLE challenges you to identify what data and algorithms best predict AD progression. This facilitates early identification of patients likely to be receptive to treatment, thus purifying cohorts for clinical trials, highlighting positive treatment effects, and facilitating translation of effective treatments to market.

Прогнозирование вероятности невозврата кредита

2 августа 2017 — 30 ноября 2017
Осталось 3 месяца, 1 неделя
Целью конкурса является разработка модели кредитного скоринга, основанная на анализе данных о кредитном поведении клиентов, полученных из внешних источников. Данные представляют собой кредитные истории: атрибуты кредитов, выданных клиенту в прошлом. Данные поступают из 4-х различных источников, идентификатор источника указан в поле NUM_SOURCE. Задача усложняется тем, что данные могут быть неполны: некоторые источники могут не содержать информацию о части выданных кредитов, противоречивы – источники могут содержать разные значения атрибутов по одному и тому же кредиту, возможны коллизии других типов. В качестве первого этапа решения задачи конкурса участникам предлагается предложить способ объединения данных нескольких источников и поиска и фильтрации выбросов.

Kaggle: Passenger Screening Algorithm Challenge

22 июня 2017 — 16 декабря 2017
Осталось 3 месяца, 3 недели
Currently, TSA purchases updated algorithms exclusively from the manufacturers of the scanning equipment used. These algorithms are proprietary, expensive, and often released in long cycles. In this competition, TSA is stepping outside their established procurement process and is challenging the broader data science community to help improve the accuracy of their threat prediction algorithms. Using a dataset of images collected on the latest generation of scanners, participants are challenged to identify the presence of simulated threats under a variety of object types, clothing types, and body types. Even a modest decrease in false alarms will help TSA significantly improve the passenger experience while maintaining high levels of security.

DrivenData: DengAI Predicting Disease Spread

1 января 2017 — 22 декабря 2017
Осталось 4 месяца
Accurate dengue predictions would help public health workers ... and people around the world take steps to reduce the impact of these epidemics. But predicting dengue is a hefty task that calls for the consolidation of different data sets on disease incidence, weather, and the environment.

Kaggle: Zillow’s Home Value Prediction (Zestimate)

24 мая 2017 — 17 января 2018
Осталось 4 месяца, 3 недели
Zillow’s Zestimate home valuation has shaken up the U.S. real estate industry since first released 11 years ago. A home is often the largest and most expensive purchase a person makes in his or her lifetime. Ensuring homeowners have a trusted way to monitor this asset is incredibly important. The Zestimate was created to give consumers as much information as possible about homes and the housing market, marking the first time consumers had access to this type of home value information at no cost.