Generative Adversarial Network is a generative model that contains a discriminator and a generator. The discriminator is a binary classifier that is trained to classify the real image as real and the ...

Abstract: Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, ...

Abstract: Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although ...

Donghao Luo and Xue Wang. ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis. In International Conference on Learning Representations, 2024. [Our paper in OpenReview]. We ...

The spatial organization of chromatophore-muscle innervation by motoneurons enables the generation of chromatophore-shaped noise, virtual or composite chromatophores, and shape elements such as lines ...