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The contrafold tools in fact recently bridged the apparent gap between the thermodynamic and the machine learning approach to RNA bioinformatics proposing to learn a parameter set for a SCFG that structurally matches the standard energy model [ 18].
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Paisitkriangkrai et al. [48] propose to learn to rank in human re-id with metric ensembles.
Later, sparse ConvNet is proposed to learn high-performance deep networks with sparse neural connections [26].
This paper is built on our previous work where we propose to learn to predict the changes in an environment.
A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold.
A probabilistic cost for training systems is also proposed to learn ranking functions using pairs of training examples.
The task-dependent codebook compression framework was proposed to learn a compression function and adapt the codebook compression [10].
Based on the stochastic optimization tools [23 26], a stochastic iterative algorithm is proposed to learn the underlying channel distribution.
Using these features, different machine learning methods (e.g. Bayesian algorithms, Decision Tree algorithms, instance-based algorithms, deep learning algorithms) have been proposed to learn driving styles.
In [8], the regret-matching learning algorithm is proposed to learn in a distributive fashion how to achieve the correlated equilibrium set in solving the VCG auction.
Here a novel approach is proposed to learn hashing functions that preserve semantic and visual similarity between images.
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