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Challenges in Machine Learning - Home | chalearn.org Reviews
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Challenges in Machine Learning - Home | chalearn.org Reviews

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Challenges in Machine Learning

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ChaLearn Looking at People

ChaLearn Looking at People. 2014 Looking At People Challenge. 2015 Looking At People CVPR Challenge. 2015 Looking At People ICCV challenge. 2016 Looking at People CVPR Challenge. 2016 Looking at People ECCV Challenge. ChaLearn LAP IsoGD and ConGD datasets. IJCNN 2017 Explainability of Learning Machines. 2016 Looking at People ECCV Challenge. End of the first round of the First Impression Challenge. Redirecting to our new website. Please visit our new website. Http:/ chalearnlap.cvc.uab.es/.

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CiML

Friday December 9, 2016, Barcelona, Spain. NIPS 2016 workshops. Centre Convencions Internacional Barcelona. Congratulations to Sebastien Treguer for winning the mini Beat AutoSKLearn contest, which we re-opened for your enjoyment. Challenges in machine learning and data science are competitions running over several weeks or months to resolve problems using provided datasets or simulated environments. CiML is a forum that. Brings together workshop organizers, platform providers, and participants to discus...

connectomics.chalearn.org connectomics.chalearn.org

ChaLearn Connectomics Challenges

Neural Connectomics Challenge #1:. From Imaging to Connectivity. The challenge is over. Congratulations to the winners! 1st prize (1st place):. Antonio Sutera, Arnaud Joly, Aaron Qiu, Vincent François-Lavet , Gilles Louppe (team AAAGV). 2nd prize (3rd place):. 3rd prize (4th place):. Lukasz Romaszko (team: Lukasz 8000). The second place participants decided not to reveal their code. The results of verifications are available for review. Information on the methods can be found in the f. Monitoring changes...

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Software - Challenges in Machine Learning

http://www.chalearn.org/software.html

Challenges in Machine Learning. Brought to you by our collaborators. An object-oriented Matlab(R) library of machine learning algorithms having performed well in past challenges, including ridge regression, SVMs, boosting, and random forests. It provides an interface to Weka. Gesture Recognition Object Package. Software used in the gesture recognition challenge. A Matlab (R) package providing models included in the Virtual Lab. Of the Causality Workbench. Partially based on the Bayes Net Toolbox. A list ...

2

Tips - Challenges in Machine Learning

http://www.chalearn.org/tips.html

Challenges in Machine Learning. Want to organize your own competition? We are now accepting proposals for review. Reviewed and accepted proposals are eligible to display the ChaLearn logo and may. With your project, we can help you finding funding and sponsors and setting up your challenge. Full ChaLearn competition organizer guidelines may be downloaded here. Ten tips to organize a winning competition:. Choose a problem with high impact (economical, humanitarian, societal, etc.). The organizers should m...

3

Sponsors - Challenges in Machine Learning

http://www.chalearn.org/sponsors.html

Challenges in Machine Learning. You donations are used to organize new events and offer prizes and travel awards to deserving participants:. ChaLearn is a taxt-exempt organization under section 501(c)(3) of the US IRS code. Your donation is tax deductible to the extent allowed by law. We are grateful to our past and present sponsors:. This project is supported by the National Science Foundation under Grants N0. ECCS-0424142. Create a free website. Create your own free website. Start your own free website.

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Challenges - Challenges in Machine Learning

http://www.chalearn.org/challenges.html

Challenges in Machine Learning. Brought to you by our collaborators . With all websites still alive for post-challenge submissions! Feature selection (NIPS 2003). Seventy five participants competed on five classification problems to make best predictions and select the smallest possible subset of relevant input variables. Features). The tasks include: cancer diagnosis from mass-spectrometry data, handwritten digit recognition, text classification, and drug discovery. Matlab software and course material.

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Challenge and Data description - ChaLearn Looking at People

http://gesture.chalearn.org/mmdata

ChaLearn Looking at People. 2014 Looking At People Challenge. 2015 Looking At People CVPR Challenge. 2015 Looking At People ICCV challenge. 2016 Looking at People CVPR Challenge. 2016 Looking at People ECCV Challenge. ChaLearn LAP IsoGD and ConGD datasets. 2016 Looking at People ECCV Challenge. End of the first round of the First Impression Challenge. Challenge and Data description. We provide scripts in Python to access the data and evaluate your predictions. The requirements are:. OpenCV 2.4.8. For eac...

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Past sponsors - CiML

http://ciml.chalearn.org/past-sponsors

We are very grateful to our sponsors who supported CiML over the years:.

connectomics.chalearn.org connectomics.chalearn.org

Setting - ChaLearn Connectomics Challenges

http://connectomics.chalearn.org/help/setting

For Imaging to Connectivity. Figure 1: Neural cultures. We are studying methods to reconstruct the effective connectivity between neurons from observations of their activity. The activity is measured by imaging the calcium influx into neurons using fluorescent molecules. To experiment with these techniques, we use cultured neurons. The nethod is also applicable to slices of brain and to in vivo recordings of the surface of the brain after it has been surgically exposed. Figure 3: Network reconstruction.

connectomics.chalearn.org connectomics.chalearn.org

5min Tutorial on Brain Science - ChaLearn Connectomics Challenges

http://connectomics.chalearn.org/help/tutorial

5min Tutorial on Brain Science. Neurons are cells that underlie the functions of the central nervous system, including the brain and spinal cord, and the peripheral nervous system consisting of ganglions and nerves outside the brain and spinal cord. Neurons vary in shape but they share the common characteristics of electrical excitability. Neurons process chemical and electrical signals and transmit these signals to neurons and other types of cells. [ Learn more on Wikipedia. Is a long and slender struct...

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Help - ChaLearn Connectomics Challenges

http://connectomics.chalearn.org/help

Setting, Prizes, Schedule. Tasks of the challenge. Tutorial, Sample code. Setting, Prizes, Schedule. What is the goal of the challenge? The goal is to reconstruct the structure of a neural network from temporal patterns of activities of neurons. The activities are obtained from video recording of calcium fluorescence imaging. [ Learn more. Are there prizes and travel grant? We will attribute 3000 USD in prizes to the winners donated by ChaLearn:. 500 USD and 1000 USD travel award (*) Award certificate.

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ICPR 2012 gesture recognition workshop (November 10-11) - ChaLearn Looking at People

http://gesture.chalearn.org/dissemination/icpr2012

ChaLearn Looking at People. 2014 Looking At People Challenge. 2015 Looking At People CVPR Challenge. 2015 Looking At People ICCV challenge. 2016 Looking at People CVPR Challenge. 2016 Looking at People ECCV Challenge. ChaLearn LAP IsoGD and ConGD datasets. IJCNN 2017 Explainability of Learning Machines. 2016 Looking at People ECCV Challenge. End of the first round of the First Impression Challenge. ICPR 2012 gesture recognition workshop (November 10-11). Next deadline: December 15,. Shuang Deng, China.

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Evaluation-as-a-Service: a serious game - CiML

http://ciml.chalearn.org/ciml2016/schedule/evaluation-as-a-service-a-serious-game

Evaluation-as-a-Service: a serious game.

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Gathering common sense knowledge: how to game it? - CiML

http://ciml.chalearn.org/ciml2016/schedule/gathering-common-sense-knowledge-how-to-game-it

Gathering common sense knowledge: how to game it? Research in gathering and reasoning about common sense knowledge has recently gained in popularity. Measuring the progress of research in this multimodal space faces many challenges. In this talk we discuss the lessons learned, and remaining challenges in creating testing datasets and associated evaluation metrics.

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Sitemap - CiML

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Challenges in Machine Learning - Home

Challenges in Machine Learning. December 2017 at NIPS: CiML 2017 workshop. Is the science of building hardware or software that can achieve tasks by learning from examples. The examples often come as {input, output} pairs. Given new inputs a trained machine can make predictions of the unknown output. Examples of machine learning tasks include:. Automatic reading of handwriting. Automatic text classification (classification of web pages; spam filtering).

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