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Therefore, the sparse dictionaries have to be jointly learned for low- and high-resolution image patches.
Kernel weights as well as kernel-based classifiers are jointly learned.
Multi-task learning is a statistical spatial feature mapping framework where identical input data from different tasks are jointly learned.
In this paper, we present a new framework for object recognition via weakly supervised metric and template learning, wherein the optimal metric and templates are jointly learned.
Jointly learned and shared emission matrix parameters (as generated by Model 2) provide an easily interpretable platform on which to compare multiple samples from the same genome without the confounding factor of different state definitions.
Global parameters that are jointly learned across all data types are the subclonal fractions f j s and, for CNA data, the sequencing yield M s per haploid DNA (which we call mass).
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DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification.
However, this is non-trivial due to the need for jointly learning underlying correlations between heterogeneous visual and tag data.
We introduced in [28] a probabilistic formulation of DMP by jointly learning the basis functions and the local force behaviors.
Furthermore, Zheng et al. [16] combined the verification and identification models by jointly learning three loss functions.
A latent structural SVM based Weakly Supervised Metric and Template Learning (WSMTL) method is designed to jointly learn the metric, the templates, and a weight vector.
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