TODO (from http://www.chalearn.org/challenges.html):
- Tunedit: Similar platform more academically oriented (phased out?).
- DrivenData: For non-profit challenges.
- Codalab: For academic challenges of greater complexity.
- Beat: A EU sponsored platform.
- Epidemium: challenges in epidemiology.
- Pascal challenges: The Pascal network is sponsoring several challenges in Machine learning.
- Challenges.gov: Challenges sponsored by the US Government.
- Ecole Normale Superieure: Datasets and challenges.
- Beaker notebook: Convert back and forth from R/Python/Javascript
- Cortana Intelligence: Azure ML platform.
- RAMP studio: The Paris-Saclay CDS Rapid Analytics Model Prototyping platform.
- Synapse: The platform on which DREAM challenges are organized.
Collaborative platforms:
- OpenML: share ML reusable frameworks.
- MLcomp: compare machine learning programs.
- E-lico: data mining portal.
- H20: open source predictive analytics platform.
- KNIME: Data mining platform.
- Quantopian: Financial data simulator + ML tutorials.
Crowdsourcing:
- Amazon Mechanical Turk: Gets you hire people from all around the world to solve your tasks. Used to label computer vision data.
- Crowdflower: Hire people to collect, filter and enhance data.
International conferences hosting challenges:
- WCCI: World congress on computational intelligence.
- ICDAR: International Conference on Document Analysis and Recognition, a bi-annual conference proposing a contest in printed text recognition. Feature extraction/selection is a key component to win such a contest.
- ICPR: In conjunction with the International Conference on Pattern Recognition, ICPR 2004, a face recognition contest is being organized.
- ICMI: Competitions on multimodal interaction
Popular challenges:
- NNGC: Neural Network Grand Challenge in time series forecasting.
- Netflix: The 1 million dollar Netflix prize, which attracted a lot of attention and broke new grounds for recommender systems.
- Robocup: Robots who play soccer, a yearly held contest.
- DELVE: A platform developed at University of Torontoto benchmark machine learning algorithms.
- CAMDA: Critical Assessment of Microarray Data Analysis, an annual conference on gene expression microarray data analysis. This conference includes a context with emphasis on gene selection, a special case of feature selection.
- TREC: Text Retrieval conference, organized every year by NIST. The conference is organized around the result of a competition. Past winners have had to address feature extraction/selection effectively.
- CASP: An important competition in protein structure prediction called Critical Assessment of Techniques for Protein Structure Prediction.
- ICAPS competitions: Competitions in planning and knowledge engineering
- MEDIAEVAL benchmarks: Benchmarking Initiative for Multimedia Evaluation. Data sharing in multimediacommons (with incremental annotations). Uses Amazon web services to allow experimentation in the cloud.
- DREAM: Dialogue for Reverse Engineering Assessments and Methods. Challenges in gene network reconstruction.
- AVEC: Audio visual Emotion Recognition Challenge and Workshop.
- CAFA: Predicting function of biological macromolecules (as well as gene-disease associations).
Data resources:
- KEEL: Knowledge Extraction based on Evolutionary Learning.
- IO Data Science: Datasets of Paris-Saclay University.