Results 61 to 70 of about 1,305,191 (158)
Cancer Management and Research, 2009 Hongbin Zhu1, Chaijie Duan1, Perry Pickhardt2, Su Wang1, Zhengrong Liang1,31Department of Radiology, 3Department of Computer Science, State University of New York, Stony Brook, NY, USA; 2Department of Radiology, University of Wisconsin Medical School ...
Hongbin Zhu +4 more
doaj
LVGG-IE: A Novel Lightweight VGG-Based Image Encryption Scheme
EntropyImage security faces increasing challenges with the widespread application of computer science and artificial intelligence. Although chaotic systems are employed to encrypt images and prevent unauthorized access or tampering, the degradation that occurs ...
Mingliang Sun +4 more
doaj +1 more source
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
, 2019 Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed.
IEEE +8 more
core +1 more source
STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction
, 2019 Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This
Su, Han, Wang, Hongnian
core +1 more source
Response of quadrant detectors to structured beams via convolution integrals. [PDF]
Journal of The Optical Society of America A-optics Image Science and Vision, 2017 We propose a new expression for the response of a quadrant detector using convolution integrals. This expression, exploiting the properties of the convolution, is easier to evaluate by hand.
J. Narag, N. Hermosa
semanticscholar +1 more source
Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy [PDF]
, 2019 Currently, Deep Convolutional Neural Networks (DCNNs) are used to solve all kinds of problems in the field of machine learning and artificial intelligence due to their learning and adaptation capabilities. However, most successful DCNN models have a high computational complexity making them difficult to deploy on mobile or embedded platforms.
arxiv +1 more source
Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds [PDF]
arXiv, 2018 Convolution has been playing a prominent role in various applications in science and engineering for many years. It is the most important operation in convolutional neural networks. There has been a recent growth of interests of research in generalizing convolutions on curved domains such as manifolds and graphs.
arxiv
Graph-Time Convolutional Neural Networks [PDF]
arXiv, 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
arxiv
Hypercontractive inequalities via SOS, and the Frankl-Rödl graph
Discrete Analysis, 2016 Hypercontractive inequalities via SOS, and the Frankl-Rödl graph, Discrete Analysis 2016:4, 20 pp. One of the major unsolved problems in theoretical computer science is the unique games conjecture of Subhash Khot. Its importance lies in the fact that it
Manuel Kauers +3 more
doaj +1 more source
Tensor graph convolutional neural network [PDF]
arXiv, 2018 In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs.
arxiv