Results 101 to 110 of about 124,232 (320)
Phase space sampling and operator confidence with generative adversarial networks
, 2017 We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning ...
Mills, Kyle, Tamblyn, Isaac
core
Advanced Sensor Research, EarlyView.A holographic sensing‐integrated deep learning platform enables real‐time, label‐free assessment of red blood cell (RBC) quality during storage. By combining diffusion model‐based data augmentation and self‐supervised pretraining, the framework achieves high segmentation accuracy with minimal data.
Seonghwan Park +3 more
wiley +1 more source
Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth
Advanced Theory and Simulations, EarlyView.A data‐driven framework combining Computational Fluid Dynamics (CFD) simulations and machine learning is proposed to model and optimize Czochralski crystal growth. Using different transfer learning strategies (Warm Start, Merged Training, and Hyperparameter Transfer) the study demonstrates improved predictions for Ge and GaAs growth from Si‐trained ...
Milena Petkovic +3 more
wiley +1 more source
Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
Advanced Science, EarlyView.AMPainter is a powerful design model for ’painting’ the antimicrobial potency on any given peptide sequence, based on the strategy of virtual directed evolution and deep reinforcement learning. Abstract In the post‐antibiotic era, antimicrobial peptides (AMPs) are considered ideal drug candidates because of their lower likelihood of inducing resistance.
Ruihan Dong, Qiushi Cao, Chen Song
wiley +1 more source
Multi-class data augmentation for prediction of postpartum hemorrhage using improved ACGAN
Alexandria Engineering JournalThe dataset of primary postpartum hemorrhage (PPH) faces the challenge of insufficient samples, and Generative Adversarial Networks (GANs) have shown considerable promise in addressing the scarcity and imbalance of samples in the diagnosis of PPH ...
Xiaodan Li +6 more
doaj +1 more source
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
, 2017 We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks.
Han, Sungyeob +2 more
core
Advanced Science, EarlyView.It is synthesized precision‐arranged DNA origami plasmonic nanoantennas for multiplexed and intelligent warning of bone loss induced by microgravity and radiation. The ordered nanoantennas simultaneously and accurately detected calcium ions, interleukin‐6, and microRNA‐214 in serum from mice exposed to microgravity and radiation, with performance ...
Yufan Ling +14 more
wiley +1 more source
Advanced Science, EarlyView.KPPepGen is a knowledge‐aware prompt diffusion model for the controllable generation of pathogen‐specific AMPs. It outperforms existing methods and is capable of simultaneously generating peptides for distinct pathogens, demonstrating superior performance with favorable properties and docking efficacy.
Yongkang Wang +4 more
wiley +1 more source
Tongxin xuebao, 2023 Aiming at the problem that the resources of maritime mobile terminals were limited and the network traffic was imbalanced in the MMSN (maritime meteorological sensor network) environment, which made it difficult to detect network intrusion accurately, a ...
Xin SUN +3 more
doaj +2 more sources
Generative Adversarial Networks: Recent Developments [PDF]
, 2019 10 ...
Maciej Zięba +3 more
openaire +3 more sources