The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a strictly ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

In file src_python/ldpc/union_find_decoder/_union_find_decoder.pyx, line 63: def __cinit__(self, pcm: Union[np.ndarray, spmatrix], uf_method: str = False):. A quick ...

1 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, China 2 College of Geophysics, Chengdu University of Technology, Chengdu, China ...

This Collection calls for submissions of original research into techniques that facilitate the advancement of deep learning for image analysis and object detection, driving computer vision forward and ...

Objective: To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. Methods: We retrospectively ...

A deep-learning powered single-strained electronic skin sensor has been developed by researchers at The Korea Advanced Institute of Science and Technology (KAIST), that can capture human motion from a ...

Abstract: In the 5G communication systems, a hybrid approach to support polar codes for control plane and LDPC codes for data plane has been identified as the channel coding solution for enhanced ...