Kaggle: Mercari Price Suggestion Challenge

21 ноября 2017 — 22 февраля 2018
Осталось 2 часа, 51 минута
In this competition, Mercari’s challenging you to build an algorithm that automatically suggests the right product prices. You’ll be provided user-inputted text descriptions of their products, including details like product category name, brand name, and item condition.

TopCoder: Intel Movidius Challenge

15 января 2018 — 26 февраля 2018
Осталось 4 дня, 2 часа
In this challenge you will be pushing your network training skills to its limits by fine-tuning convolutional neural networks (CNNs) that are targeted for embedded applications. Contestants are expected to leverage the Neural Compute SDK's (NCSDK) mvNCProfile tool to analyze the bandwidth, execution time and complexity of their network at each layer, and tune it to get the best accuracy and execution time.

crowdAI: WWW 2018 Challenge Learning to Recognize Musical Genre

1 февраля 2018 — 28 февраля 2018
Осталось 6 дней, 2 часа
Like never before, the web has become a place for sharing creative work - such as music - among a global community of artists and art lovers. While music and music collections predate the web, the web enabled much larger scale collections. Whereas people used to own a handful of vinyls or CDs, they nowadays have instant access to the whole of published musical content via online platforms. Such dramatic increase in the size of music collections created two challenges: (i) the need to automatically organize a collection (as users and publishers cannot manage them manually anymore), and (ii) the need to automatically recommend new songs to a user knowing his listening habits. An underlying task in both those challenges is to be able to group songs in semantic categories.

3-D Validation of Tractography with Experimental MRI (3D VoTEM)

15 декабря 2017 — 4 марта 2018
Осталось 1 неделя, 3 дня
The proposed challenge is the first diffusion tractography challenge to include a three-dimensional physical phantom (sub-challenge #1), which models the intricate details of brain white matter microstructure, such as crossing and bending fibers, and allows testing of an array of acquisition conditions and tractography methods intended to extract these features. Additionally, this is the first tractography challenge to include histologically derived white matter tracts (sub-challenge #2 and #3). Histology adds an extra layer of realism to both data acquisition and potential tractographic challenges –including not only the true anatomical complexity of white matter pathways, but also tracer sensitivity on the scale of microns. The histological challenges, then, are to most accurately replicate the anatomical connections as identified by the histological tracers. Together, these three sub-challenges present a unique three-dimensional opportunity to validate diffusion tractography. These are offered in combination with our industry sponsor, Synaptive Medical, as well as NIH funded research (Anderson, Landman).

Boosters: Raiffeisen Data Cup

9 февраля 2018 — 23 марта 2018
Осталось 4 недели, 1 день
Клиенты Райффайзенбанка совершают покупки и снимают наличные в банкоматах с помощью карточек. Получив в виде обезличенных данных их историю транзакций, информацию о мерчантах (место, позволяющее принимать платежи с использованием банковской пластиковой карты), участники чемпионата должны предсказать две пары координат: дом и работу клиента. Оценкой качества решения в задаче является процент попаданий в окружность радиуса 0.02 градуса относительно реальных координат дома и работы.

DrivenData: Power Laws Detecting Anomalies in Usage

6 февраля 2018 — 31 марта 2018
Осталось 1 месяц, 1 неделя
The building systems may fail to meet the performance expectations due to various faults. Poorly maintained, degraded, and improperly controlled equipment wastes an estimated 15% to 30% of energy used in commercial buildings. Therefore, it is of great potential to develop automatic, quick-responding, accurate and reliable fault detection and to provide diagnosis schemes to ensure the optimal operations of systems to save energy. Schneider Electric already has relevant offers, but would like to determine if alternative techniques can add new detections / functionalities, bring gain in precision, or operate with less data.

DrivenData: Power Laws Optimizing Demand-side Strategies

6 февраля 2018 — 31 марта 2018
Осталось 1 месяц, 1 неделя
Flexibility can be produced in different manners. It might come from generation options, from energy storage or from energy demand. In some cases, generation can also be proposed through alternative dispatchable assets such as Combined Heat and Power (CHP). Storage is valid for both electricity and heat. Energy storage is an easy way to increase building flexibility, provided there is a business case for such an investment. The present challenge is focused on making a good usage of an installed storage system. Viewed from the demand side, in the case of smart buildings, time of use tariffs incite to use energy when it is the most available. Given such a tariff, the goal is to buy more energy when its price is the lowest, and buy less (or possibly sell) energy when its price is the highest. Your goal in this competition is to build an algorithm that controls a battery charging system and spends the least amount of money over a simulation period.

DrivenData: Power Laws Forecasting Energy Consumption

8 февраля 2018 — 31 марта 2018
Осталось 1 месяц, 1 неделя
Planning and forecasting the use of the electrical energy is the backbone of effective operations. Energy demand forecasting is used within multiple Schneider Electric offers, and different methods require more or less data. Schneider Electric is interested in more precise and robust forecasting methods that do well with little data. The goal is to improve the best estimation of the global consumption for a building.

Kaggle: 2018 Data Science Bowl

16 января 2017 — 17 апреля 2018
Осталось 1 месяц, 3 недели
By participating, teams will work to automate the process of identifying nuclei, which will allow for more efficient drug testing, shortening the 10 years it takes for each new drug to come to market.

Kaggle: Google Landmark Recognition Challenge

3 февраля 2018 — 22 мая 2018
Осталось 2 месяца, 4 недели
Many Kagglers are familiar with image classification challenges like the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which aims to recognize 1K general object categories. Landmark recognition is a little different from that: it contains a much larger number of classes (there are a total of 15K classes in this challenge), and the number of training examples per class may not be very large. Landmark recognition is challenging in its own way.

Kaggle: Google Landmark Retrieval Challenge

3 февраля 2018 — 22 мая 2018
Осталось 2 месяца, 4 недели
In this competition, Kagglers are given query images and, for each query, are expected to retrieve all database images containing the same landmarks (if any).