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Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.
Naturally, the startup will also continue investing in the development of api.ai's "deep learning methods for speech recognition, intent classification and dialog management," which points to one of the main drivers behind moving to a largely free-to-use model.
I'm a researcher and consultant specializing on artificial intelligence, including deep learning methods for computer vision, natural language processing, and generative adversarial networks.
This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma.
This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost.
The challenge received many contributions using deep learning methods for feature extraction and personality prediction.
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Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data.
Deep learning methods have been shown to currently offer the most efficient acoustic modeling framework for ASR.
To address this, machine learning and deep learning methods have been used and achieved promising results.
Unsupervised (or deep) learning methods [12].
Deep learning methods are representation learning methods [20], which allow a machine to be fed with raw data and to automatically learn the representations needed for classification.
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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