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The parameters of the models are learned jointly with the target of the specific task.
Moreover, recently emerged deep neural networks for sequence prediction, e.g., Long Short-Term Memory models, can potentially boost predictions when learned jointly on text and affect signals.
In the detailed implementation, a quality-aware regularization term is added to the traditional dictionary learning formulation, such that a feature-aware dictionary and a quality-aware dictionary can be learned jointly.
The model parameters are learned jointly with Gibbs sampling; the Gibbs-formulas are presented in Section 2.6.
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We created a neural network that is able to perform the same function as the BoVW but is able to learn jointly the feature extraction, coding, and classification functions.
The idea of sharing information learnt across sub-tasks seems a more economical use of data, where presumably all tasks are learnt jointly.
The experiences of, and benefits for students, junior doctors and registrars learning jointly in a general practice have had minimal focus in the literature.
It is shown that the proposed JSDA algorithm not only can learn the jointly sparse discriminant vectors to explore the key factors of the breast cancer in cancer pathologic diagnosis, but also can improve the diagnosis accuracy compared with the classical feature extraction and discriminant analysis algorithm.
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.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com