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GenONet is a novel deep learning architecture that uses Deep Operator Networks within a Generative Adversarial Network framework for high-resolution precipitation nowcasting, producing sharp and physically consistent forecasts.
This paper proposes GraphGAN, a graph-based generative adversarial network for adaptive DDoS attack detection in next-generation networks, addressing class imbalance and achieving superior performance on benchmark datasets.
This paper presents a model-guided framework using AI to discover and generate facial emotion stimuli that maximize perceptual differences between autistic and neurotypical individuals, demonstrating that group differences are concentrated in a small subset of expressions.
Proposes CAGI, a framework that integrates clustering and generative adversarial networks to improve missing data imputation by exploiting latent subgroup structures, achieving superior performance on benchmark datasets.