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Convolutional Neural Network (CNN): A comprehensive overview
International Journal of Multidisciplinary Research and Growth Evaluation, 2022 Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN
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Continuous speech recognition by convolutional neural networks
工程科学学报, 2015 Convolutional neural networks (CNNs), which show success in achieving translation invariance for many image processing tasks, were investigated for continuous speech recognition.
ZHANG Qing-qing +3 more
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A-CNN: Annularly Convolutional Neural Networks on Point Clouds [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution.
Artem Komarichev +2 more
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Understanding of Convolutional Neural Network (CNN): A Review
International Journal of Robotics and Control Systems, 2023 The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans.
Purwono Purwono +5 more
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Attention-Based Convolutional LSTM for Describing Video
IEEE Access, 2020 Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the
Zhongyu Liu +4 more
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Imaging from temporal data via spiking convolutional neural networks [PDF]
, 2020 A new approach for imaging that is solely based on the time of flight of photons coming from the entire imaged scene, combined with a novel machine learning algorithm for image reconstruction: a spiking convolutional neural network (SCNN) named Spike-SPI
Kapitany, Valentin +6 more
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Clifford-steerable convolutional neural networks [PDF]
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of E(p,q)-equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean spaces Rp,q.
Weiler, M. +6 more
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Robust stability for stochastic Hopfield neural networks with time delays [PDF]
, 2006 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic ...
Liu, X, Wang, Z, Shu, H, Fang, J
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Understanding convolutional neural networks [PDF]
, 2021 In the past decade, deep learning has fueled a number of exciting developments in artificial intelligence (AI). However, as deep learning is increasingly being applied to high-impact domains, like medical diagnosis or autonomous driving, the impact of ...
Fong, Ruth
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Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications
Frontiers in Neuroscience, 2020 In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice ...
Martino Sorbaro +4 more
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