Results 61 to 70 of about 3,605,315 (303)
Advanced Healthcare Materials, EarlyView.Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei +9 more
wiley +1 more source
CNNs Avoid the Curse of Dimensionality by Learning on Patches
IEEE Open Journal of Signal Processing, 2023 Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori ...
Vamshi C. Madala +2 more
doaj +1 more source
Robust Local Cluster Neural Networks (ESANN) [PDF]
, 2006 Eickhoff R, Sitte J, Rückert U. Robust Local Cluster Neural Networks (ESANN). In: Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN).
Eickhoff, Ralf +3 more
core
Advanced Healthcare Materials, EarlyView.NeuroVisio SyncPatch integrates skin‐conformal electromyography (EMG) electrodes with camera‐tracked markers to jointly assess muscle activity and three‐dimensional knee kinematics during rehabilitation. Parallel feedback pathways provide real‐time kinematic guidance during movement and post‐trial neuromuscular feedback.
Bohyung Choi +11 more
wiley +1 more source
Structural Compression of Convolutional Neural Networks with Applications in Interpretability
Frontiers in Big Data, 2021 Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CNNs make them difficult for human interpretation or understanding in ...
Reza Abbasi-Asl +4 more
doaj +1 more source
Strategies in Jpeg Compression Using Convolutional Neural Network(Cnn) [PDF]
SSRN Electronic Journal, 2021 Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This
openaire +2 more sources
Ferroelectric Quantum Dots for Retinomorphic In‐Sensor Computing
Advanced Materials, EarlyView.This work has provided a protocol for fabricating retinomorphic phototransistors by integrating ferroelectric ligands with quantum dots. The resulting device combines ferroelectricity, optical responsiveness, and low‐power operation to enable adaptive signal amplification and high recognition accuracy under low‐light conditions, while supporting ...
Tingyu Long +26 more
wiley +1 more source
Powerset convolutional neural networks
, 2019 We present a novel class of convolutional neural networks (CNNs) for set functions,i.e., data indexed with the powerset of a finite set. The convolutions are derivedas linear, shift-equivariant functions for various notions of shifts on set functions.The
Püschel, Markus +2 more
core +2 more sources
Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users
Applied SciencesIn recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems.
Ilaria Cacciari, Anedio Ranfagni
doaj +1 more source
CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction [PDF]
Interdisciplinary Sciences: Computational Life Sciences, 2018 Accurate gene prediction in metagenomics fragments is a computationally challenging task due to the short-read length, incomplete, and fragmented nature of the data. Most gene-prediction programs are based on extracting a large number of features and then applying statistical approaches or supervised classification approaches to predict genes.
Amani Al-Ajlan, Achraf El Allali
openaire +2 more sources