Results 71 to 80 of about 222,396 (351)
Generative Adversarial Networks in finance: an overview
, 2021 Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications.
Eckerli, Florian, Osterrieder, Joerg
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Advanced Functional Materials, EarlyView.Advances in integrating artificial intelligence into 3D bioprinting are systematically reviewed here. Machine learning, computer vision, robotics, natural language processing, and expert systems are examined for their roles in optimizing bioprinting parameters, real‐time monitoring, quality control, and predictive maintenance.
Joao Vitor Silva Robazzi +10 more
wiley +1 more source
SFCWGAN-BiTCN with Sequential Features for Malware Detection
Applied Sciences, 2023 In the field of adversarial attacks, the generative adversarial network (GAN) has shown better performance. There have been few studies applying it to malware sample supplementation, due to the complexity of handling discrete data.
Bona Xuan, Jin Li, Yafei Song
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Beautification of images by generative adversarial networks
Journal of Vision, 2023 Finding the properties underlying beauty has always been a prominent yet difficult problem. However, new technological developments have often aided scientific progress by expanding the scientists' toolkit. Currently in the spotlight of cognitive neuroscience and vision science are deep neural networks.
Music, Amar +2 more
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Advanced Functional Materials, EarlyView.This review discusses cellulose‐based hydrogels technology, analyzes their application progress in physiological signal monitoring, and explores the effects of pretreatment, crosslinking, and molding methods on gel performance, to provide valuable insights into the efficient utilization of plant fibers and the environmentally friendly development of ...
Zhiming Wang +8 more
wiley +1 more source
HGAN: Hyperbolic Generative Adversarial Network
IEEE Access, 2021 Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data.
Diego Lazcano +2 more
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Adversarial Variational Optimization of Non-Differentiable Simulators [PDF]
, 2019 Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations.
Cranmer, Kyle +2 more
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CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
, 2017 We present variational generative adversarial networks, a general learning framework that combines a variational auto-encoder with a generative adversarial network, for synthesizing images in fine-grained categories, such as faces of a specific person or
Bao, Jianmin +4 more
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Self-Sparse Generative Adversarial Networks
CAAI Artificial Intelligence Research, 2022 Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem.
Wenliang Qian +3 more
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Advanced Materials, EarlyView.This review summarizes the latest progress in transforming renewable bamboo resources into high‐value functional materials. It emphasizes how to leverage bamboo's multiscale hierarchical structures to realize innovative applications in energy, construction, environment, and medicine, providing key insights for developing high‐performance bio‐based ...
Yuxiang Huang +13 more
wiley +1 more source