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A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold.
In this paper, a Conditional Random Fields (CRF) based model is proposed to learn latent semantics of PDF page content.
Here a novel approach is proposed to learn hashing functions that preserve semantic and visual similarity between images.
This study proposed to learn more about the practice of midwifery from a diverse sample of midwives.
The Max-margin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework.
In this paper, a technique based on neural networks is proposed to learn the inverse kinematics mapping between the fingertip 3D position and the corresponding joint angles.
Similar(36)
This paper is built on our previous work where we propose to learn to predict the changes in an environment.
We propose to learn an extremely compact visual descriptor from the mobile contexts towards low bit rate mobile location search.
Here we propose to learn discriminative unary features and enhance thin vessels for pairwise potentials to further improve the segmentation performance.
To obtain the membership functions, we propose to learn the relationship between the computational values given by the measures and the human perception of fineness.
In this paper, we propose to learn the structures of stereoscopic image based on convolutional neural network (CNN) for no-reference quality assessment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com