It introduces a novel framework combining a sparse deformable marching cubes structure called Sparcubes with a modality-consistent autoencoder known as Sparconv-VAE. Sparcubes transforms raw mesh data ...

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 ...

This difference often exhibits spatial proximity and is likely to satisfy computational continuity, where sparse convolution can achieve good results with a simple approach. However, the effective ...

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 ...

A new technical paper titled “Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention” was published by DeepSeek, Peking University and University of Washington. Abstract ...

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ...

The core of the Sparse Autoencoder implementation follows a straightforward encoder-decoder architecture with design choices following mainly the choices made by OpenAI.