Results 31 to 40 of about 250,191 (314)
Parallel accelerator design for convolutional neural networks based on FPGA
Dianzi Jishu Yingyong, 2021 In recent years, convolutional neural network plays an increasingly important role in many fields. However, power consumption and speed are the main factors limiting its application.
Wang Ting, Chen Binyue, Zhang Fuhai
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Convolutional neural networks in APL [PDF]
Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, 2019 This paper shows how a Convolutional Neural Network (CNN) can be implemented in APL. Its first-class array support ideally fits that domain, and the operations of APL facilitate rapid and concise creation of generically reusable building blocks. For our example, only ten blocks are needed, and they can be expressed as ten lines of native APL. All these
Artjoms Sinkarovs +2 more
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SC-PNN: Saliency Cascade Convolutional Neural Network for Pansharpening [PDF]
, 2021 In many remote sensing tasks, different types of regions or targets differ in requirements for spectral and spatial quality. The discrepancy reveals that a uniform pansharpening strategy applying to the entire image may not fulfill the varying demands of
Zhang, Jue +3 more
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Chemically Inspired Convolutional Neural Network using Electronic Structure Representation [PDF]
, 2023 In recent years, a development of appropriate crystal representations for accurate prediction of inorganic crystal properties has been considered as one of the essential tasks to accelerate materials discovery through high-throughput virtual screening ...
Seoin, Back +3 more
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Canonical convolutional neural networks
2022 International Joint Conference on Neural Networks (IJCNN), 2022 We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode ...
Lokesh Veeramacheneni +3 more
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Forecasting Nonadiabatic Dynamics using Hybrid Convolutional Neural Network/Long Short-Term Memory Network [PDF]
, 2021 Modeling nonadiabatic dynamics in complex molecular or condensed-phase systems has been challenging especially for the long-time dynamics. In this work, we propose a time series machine learning scheme based on the hybrid convolutional neural network ...
Jiebo, Li +3 more
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Powerset Convolutional Neural Networks
CoRR, 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 derived as linear, shift-equivariant functions for various notions of shifts on set functions.
Wendler, Chris +2 more
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A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting [PDF]
, 2020 Forecasting the traffic flow is a critical issue for researchers and practitioners in the field of transportation. Using the graph convolutional network (GCN) is widespread in traffic flow forecasting. Existing GCN-based methods mostly rely on undirected
Zhaoyang Li +7 more
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Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images [PDF]
, 2023 The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic.
Salam Abdulkhaleq Noaman +2 more
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Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of ...
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