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Capsule networks–a survey
M Kwabena Patrick, A Felix Adekoya… - Journal of King Saud …, 2019 - Elsevier
Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …
recognition, natural language processing, object detection, object segmentation and …
Capsule networks–a survey
M Kwabena Patrick, A Felix Adekoya, A Abra Mighty… - 2022 - dl.acm.org
Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …
recognition, natural language processing, object detection, object segmentation and …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …
their unmatchable performance in several applications, such as image processing, computer …
NASCaps: A framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks
Deep Neural Networks (DNNs) have made significant improvements to reach the desired
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …
Random CapsNet forest model for imbalanced malware type classification task
Behavior of malware varies depending the malware types, which affects the strategies of the
system protection software. Many malware classification models, empowered by machine …
system protection software. Many malware classification models, empowered by machine …
Q-capsnets: A specialized framework for quantizing capsule networks
Capsule Networks (CapsNets), recently proposed by the Google Brain team, have superior
learning capabilities in machine learning tasks, like image classification, compared to the …
learning capabilities in machine learning tasks, like image classification, compared to the …
Enabling capsule networks at the edge through approximate softmax and squash operations
Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high
learning capabilities at the cost of compute-intensive operations. To enable their deployment …
learning capabilities at the cost of compute-intensive operations. To enable their deployment …
Rohnas: A neural architecture search framework with conjoint optimization for adversarial robustness and hardware efficiency of convolutional and capsule networks
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network
(DNN) architectures for a given application under given system constraints. DNNs are …
(DNN) architectures for a given application under given system constraints. DNNs are …
FEECA: Design space exploration for low-latency and energy-efficient capsule network accelerators
In the past few years, Capsule Networks (CapsNets) have taken the spotlight compared to
traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs …
traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs …