The goal of the Master Challenge is to derive specification of the HF sky-wave environment (the bottom-side ionosphere) across a longer circuit. This will be accomplished through passive reception of active sounders from an Oblique Incidence (OI). This process will require modification of the solver algorithms developed in the Explorer Challenge to determine ionospheric characteristics derived from more difficult OI datasets.
The objective of this contest is as follows:
Task 1/ Task 2: Develop a robust model to predict Free Lime.
Task 3: Identify significant parameters which influence the target variable.
Variable to be predicted (Free Lime): Output Parameter
Outcome: The successful solvers will have created the most accurate and robust model to predict Free Lime on the data provided.
The objective of this competition is to create a machine learning model to detect fraudulent transactions.
Fraud detection is an important application of machine learning in the financial services sector. This solution will help Xente provide improved and safer service to its customers.
In this challenge, we are looking for solutions for the vehicle routing problem (VRP) to minimize the total cost for 7300 days (20 years). We will give you 9 datasets. You will need to come up with a feasible schedule for some of these dataset and minimize the cost as much as possible. Check the ‘Final Score’ section.
In previous years, participants worked on advancements in video-level annotations, building both unconstrained and constrained models. In this third challenge based on the YouTube 8M dataset, Kagglers will localize video-level labels to the precise time in the video where the label actually appears, and do this at an unprecedented scale. To put it another way: at what point in the video does the cat sneeze?
This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments.
The objective of the new ChaLearn AutoDL challenge series, organized with Google and 4Paradigm, is to address some of the limitations of the previous challenges and provide an ambitious benchmark multi-class classification problems without any human intervention, in limited time, on any large-scale dataset composed of samples either in tabular format, 2d matrices, time series, or spatio-temporal series. Data are formatted in a uniform way as 4d tensors (t, x, y, channel). This lends itself in particular to the use of convolutional neural networks (CNNs). Although the use of TensorFlow is facilitated by providing participants with a starting kit including sample code demonstrating how to solve the problems at hand with TensorFlow, the participants are free to provide solutions not using TensorFlow.
In this competition, you’ll benchmark machine learning models on a challenging large-scale dataset. The data comes from Vesta's real-world e-commerce transactions and contains a wide range of features from device type to product features. You also have the opportunity to create new features to improve your results.
In this track of the Challenge, you are asked to provide segmentation masks of objects.
This track’s training set represents 2.1M segmentation masks for object instances in 300 categories; with a validation set containing an additional 23k masks. The train set masks were produced by our state-of-the-art interactive segmentation process, where professional human annotators iteratively correct the output of a segmentation neural network. The validation and test set masks have been annotated manually with a strong focus on quality.
In this track of the Challenge, you are asked to detect pairs of objects and the relationships that connect them.
The training set contains 329 relationship triplets with 375k training samples. These include both human-object relationships (e.g. "woman playing guitar", "man holding microphone"), object-object relationships (e.g. "beer on table", "dog inside car"), and also considers object-attribute relationships (e.g."handbag is made of leather" and "bench is wooden").
In this challenge, the task is to detect truly suspicious events and false alarms within the set of so-called network traffic alerts, that the Security Operations Center (SOC) Team members @ SOD have to analyze on an everyday basis. An efficient classification model could help the SOC Team to optimize their operations significantly. It is worth adding that although the competition sponsor is entirely commercial, the knowledge and experience that can be gathered by the competition participants may be highly beneficial to improve the intelligent cybersecurity modules in many organizations.
Your task will be to output a detailed graph structure with edges corresponding to roadways and nodes corresponding to intersections and end points, with estimates for route travel times on all detected edges.
The hosts need help from machine learning experts to transcribe Kuzushiji into contemporary Japanese characters. With your help, Center for Open Data in the Humanities (CODH) will be able to develop better algorithms for Kuzushiji recognition. The model is not only a great contribution to the machine learning community, but also a great help for making millions of documents more accessible and leading to new discoveries in Japanese history and culture.
In this track of the Challenge, you are asked to predict a tight bounding box around object instances.
The training set contains 12.2M bounding-boxes across 500 categories on 1.7M images. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (7 per image on average).
The objective of this Zindi competition is to create a machine learning model capable of predicting the humidity for a particular plot in the next few days, using data from the past. A part of the challenge is to design algorithms that are resilient and can be trained with incomplete data (e.g. missing data points) and unclean data (e.g. lot of outliers).
In this competition, you’ll help engineers improve the algorithm by localizing and classifying surface defects on a steel sheet.
If successful, you’ll help keep manufacturing standards for steel high and enable Severstal to continue their innovation, leading to a stronger, more efficient world all around us.
Необходимо разработать алгоритм, который способен успешно ответить на вопросы экзаменационного теста, основываясь на информации из открытых источников. Участникам предоставляются тестовые варианты заданий, которые можно использовать для валидации решений и для обучения. Решения участников отправляются в автоматическую проверяющую систему и оцениваются на скрытом наборе вопросов.