Results 51 to 60 of about 1,645,295 (287)
Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives
Energies, 2023 In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science.
Dorian Skrobek +8 more
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Reconstructing Training Data from Trained Neural Networks [PDF]
Neural Information Processing Systems, 2022 Understanding to what extent neural networks memorize training data is an intriguing question with practical and theoretical implications. In this paper we show that in some cases a significant fraction of the training data can in fact be reconstructed ...
Niv Haim +4 more
semanticscholar +1 more source
Computing with dynamic attractors in neural networks [PDF]
Biosystems, 1995 In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points.
Hirsch, MW, Baird, B
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A neural network for shortest path computation [PDF]
IEEE Transactions on Neural Networks, 2001 This paper presents a new neural network to solve the shortest path problem for inter-network routing. The proposed solution extends the traditional single-layer recurrent Hopfield architecture introducing a two-layer architecture that automatically guarantees an entire set of constraints held by any valid solution to the shortest path problem.
F. Araújo, B. Ribeiro, L. Rodrigues
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Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey [PDF]
Machine Learning and Knowledge Extraction, 2019 Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not ...
Vanessa Buhrmester +2 more
semanticscholar +1 more source
Automatic Gully Detection: Neural Networks and Computer Vision
Remote Sensing, 2020 Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas.
Artur M. Gafurov, Oleg P. Yermolayev
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Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks
Applied Sciences, 2020 Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD).
Muhammad Naseer Bajwa +7 more
semanticscholar +1 more source
Small Neural Networks can Denoise Image Textures Well: a Useful Complement to BM3D
Image Processing On Line, 2016 Recent years have seen a surge of interest in deep neural networks fueled by their successful applications in numerous image processing and computer vision tasks. However, such applications typically come with huge computational loads.
Yi-Qing Wang
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SuperGlue: Learning Feature Matching With Graph Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2019 This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Paul-Edouard Sarlin +3 more
semanticscholar +1 more source
Harnessing Advanced Neural Architectures: A Comprehensive Approach to Stock Market Prediction Using ANN, BPNN, and GAN [PDF]
SHS Web of ConferencesThe advent of advanced neural network models has revolutionized the field of machine learning, enabling breakthroughs in various domains such as computer vision, natural language processing, and predictive analytics.
Wang Yang
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