Results 1 to 10 of about 52,284 (313)
A taxonomy of Deep Convolutional Neural Nets for Computer Vision [PDF]
Frontiers in Robotics and AI, 2016 Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features.
Suraj eSrinivas +5 more
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Fully convolutional neural nets in-the-wild [PDF]
Remote Sensing Letters, 2020 The ground breaking performance of fully convolutional neural nets (FCNs) for semantic segmentation tasks has yet to be achieved for landcover classification, partly because a lack of suitable trai...
Daniel M. Simms
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Entangled q-convolutional neural nets
Machine Learning: Science and Technology, 2021 We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neural networks and admitting a tensor network description. As examples, we apply q-CNN to the MNIST and Fashion MNIST classification tasks. We explain how the network associates a quantum state to each classification label, and study the entanglement ...
Vassilis Anagiannis, Miranda C. N. Cheng
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ROADSIDE FOREST MODELING USING DASHCAM VIDEOS AND CONVOLUTIONAL NEURAL NETS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Tree failure is a primary cause of storm-related power outages throughout the United States. Roadside vegetation management is therefore critical to electric utility companies to prevent power outages during extreme weather conditions. It is difficult to
D. Joshi, C. Witharana
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Communication-Optimal Convolutional Neural Nets [PDF]
, 2018 Efficiently executing convolutional neural nets (CNNs) is important in many machine-learning tasks. Since the cost of moving a word of data, either between levels of a memory hierarchy or between processors over a network, is much higher than the cost of an arithmetic operation, minimizing data movement is critical to performance optimization.
James Demmel, Grace Dinh
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Convolutional neural nets in chemical engineering: Foundations, computations, and applications [PDF]
AIChE Journal, 2021 AbstractIn this article, we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: (i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, (ii) demystifying underlying computations, and (iii) identifying new types of applications. CNNs
Shengli Jiang, Víctor M. Zavala
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Inferring depth contours from sidescan sonar using convolutional neural nets [PDF]
IET Radar, Sonar & Navigation, 2019 Sidescan sonar images are 2D representations of the seabed. The pixel location encodes distance from the sonar and along track coordinate. Thus one dimension is lacking for generating bathymetric maps from sidescan. The intensities of the return signals do, however, contain some information about this missing dimension.
Yiping Xie, Nils Bore, John Folkesson
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Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs [PDF]
IEEE Access, 2020 Understanding driver activity is vital for in-vehicle systems that aim to reduce the incidence of car accidents rooted in cognitive distraction. Automating real-time behavior recognition while ensuring actions classification with high accuracy is however
Chaoyun Zhang +4 more
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Network Inversion of Convolutional Neural Nets [PDF]
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios.
Pirzada Suhail, Amit Sethi
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Network Inversion of Convolutional Neural Nets (Student Abstract)
Proceedings of the AAAI Conference on Artificial IntelligenceNeural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios.
Pirzada Suhail, Amit Sethi
openalex +3 more sources