Combined with hypergraph convolution and Fourier KAN techniques, NCRAE achieves effective node embedding learning. Experiments on cancer-related ceRNA data sets show that NCRAE outperforms existing ...

On the basis of references [12] [13], we have further expanded and formed the SK deconvolution module, which combines the SK convolution module with FCAE to propose a stacked convolutional autoencoder ...

ESC: Emulating Self-attention with Convolution for Efficient Image Super-Resolution In this paper, we tackle the high computational overhead of transformers for lightweight image super-resolution. (SR ...

In this paper, we propose an effective Convolutional Autoencoder (AE) model for Sparse Representation (SR) in the Wavelet Domain for Classification (SRWC). The proposed approach involves an ...

In this viewpoint, we briefly review recently developed autoencoder-based models designed to enhance the conformational exploration of IDPs through embedding and latent sampling.

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Keywords: protein system, conformational space, variational autoencoder, molecular dynamics, deep learning Citation: Tian H, Jiang X, Trozzi F, Xiao S, Larson EC and Tao P (2021) Explore Protein ...