[HTML][HTML] WildARe-YOLO: A lightweight and efficient wild animal recognition model
For the protection of endangered species and successful wildlife population monitoring, wild
animal recognition is essential. While deep learning models like YOLOv5 have shown …
animal recognition is essential. While deep learning models like YOLOv5 have shown …
Model partition defense against gan attacks on collaborative learning via mobile edge computing
CW Ching, TC Lin, KH Chang… - … 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
With growing concerns about privacy issues of machine learning, collaborative learning (CL)
is developed to offer on-device training. However, adversarial behaviors of model inversion …
is developed to offer on-device training. However, adversarial behaviors of model inversion …
[PDF][PDF] Classification of X-Ray Images of the Chest Using Convolutional Neural Networks.
L Mochurad, A Dereviannyi, U Antoniv - IDDM, 2021 - ceur-ws.org
A proven way to detect various injuries: from fractures to heart failure, is an X-ray. However,
because this examination method depends on the doctor's visual analysis, it can lead to …
because this examination method depends on the doctor's visual analysis, it can lead to …
Performance optimization of allreduce operation for multi-gpu systems
A Nukada - 2021 IEEE International Conference on Big Data …, 2021 - ieeexplore.ieee.org
Allreduce is one of the important collective commu-nications used in distributed deep
learning. We present a novel hybrid allreduce algorithm optimized for multi-GPU systems …
learning. We present a novel hybrid allreduce algorithm optimized for multi-GPU systems …
Image classification using CNN with multi-core and many-core architecture
D Datta, SB Jamalmohammed - Applications of Artificial Intelligence …, 2021 - igi-global.com
Image classification is a widely discussed topic in this era. It covers a vivid range of
application domains like from garbage classification applications to advanced fields of …
application domains like from garbage classification applications to advanced fields of …
Eventgrad: Event-triggered communication in parallel stochastic gradient descent
Communication in parallel systems consumes significant amount of time and energy which
often turns out to be a bottleneck in distributed machine learning. In this paper, we present …
often turns out to be a bottleneck in distributed machine learning. In this paper, we present …
Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency
Synchronous SGD with data parallelism, the most popular parallelization strategy for CNN
training, suffers from the expensive communication cost of averaging gradients among all …
training, suffers from the expensive communication cost of averaging gradients among all …
Communication-efficient local stochastic gradient descent for scalable deep learning
Synchronous Stochastic Gradient Descent (SGD) with data parallelism, the most popular
parallel training strategy for deep learning, suffers from expensive gradient communications …
parallel training strategy for deep learning, suffers from expensive gradient communications …
Communicating Neural Network architectures for resource constrained systems
P Abudu - 2022 - ora.ox.ac.uk
The deployment of millions of embedded sensors plagued by resource constraints in
sophisticated, complex and dynamic Internet of Things (IoT) environments continues to …
sophisticated, complex and dynamic Internet of Things (IoT) environments continues to …
Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing
The objective of this project is to address challenges in the design and development of
scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop …
scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop …