|
1 March - 30 December 2008
The GRavitational lEnsing Accuracy Testing 2008 (GREAT08)
Challenge focuses on a problem that is of crucial importance for future observations in cosmology. The shapes of distant galaxies can be used to determine the properties of dark energy and the nature of gravity, because light from those galaxies is bent by gravity from the intervening dark matter. The observed galaxy images appear distorted, although only slightly, and their shapes must be precisely disentangled from the effects of pixelisation, convolution and noise.
15 December 2007 - 30 April 2008
The focus of this challenge is on predicting the results of actions performed by an external agent. Examples of that problem are found, for instance, in the medical domain, where one needs to predict the effect of a drug prior to administering it, or in econometrics, where one needs to predict the effect of a new policy prior to issuing it. We focus on a given target variable to be predicted (e.g. health status of a patient) from a number of candidate predictive variables (e.g. risk factors in the medical domain). Under the actions of an external agent, variable predictive power and causality are tied together. For instance, both smoking and coughing may be predictive of lung cancer (the target) in the absence of external intervention; however, prohibiting smoking (a possible cause) may prevent lung cancer, but administering a cough medicine to stop coughing (a possible consequence) would not.
1 December 2007 - 31 March 2008
Listeners outperform automatic speech recognition systems at every level of speech recognition, including the very basic level of consonant recognition. What is not clear is where the human advantage originates. Does the fault lie in the acoustic representations of speech or in the recogniser architecture, or in a lack of compatibility between the two? There have been relatively few studies comparing human and automatic speech recognition on the same task, and, of these, overall identification performance is the dominant metric. However, there are many insights which might be gained by carrying out a far more detailed comparison.
The purpose of this challenge is to promote focused human-computer comparisons on a task involving consonant identification in noise, with all participants using the same training and test data. Training and test data and native listener and baseline recogniser results will be provided by the organisers, but participants are encouraged to also contribute listener responses. |
1 January 2007 - 1 January 2008
The goal of the Web Spam Challenge series is to identify and compare Machine Learning (ML) methods for automatically labeling structured data represented as graphs. More precisely, we focus on the problem of labeling all nodes of a graph from a partial labeling of them. The application we study is Web Spam Detection, where we want to detect deliberate actions of deception aimed at the ranking functions used by search engines.
1 January - 31 October 2007
The goal of this challenge is to recognise objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are:
Person: person
Animal: bird, cat, cow, dog, horse, sheep
Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train
Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor
1 October 2006 - 21 September 2007
The objective of the Challenge is to design a statistical machine learning algorithm that discovers which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are basic vocabulary units suitable for different tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling.
The scientific goals are:
To learn of the phenomena underlying word construction in natural languages
To discover approaches suitable for a wide range of languages
To advance machine learning methodology
|