Biological datasets, such as gene expression data, often suffer from high dimensionality, containing numerous irrelevant or redundant features that can lead to overfitting and increased computational ...

Transfer learning methodologies predominantly concentrate on adapting to varied operational conditions. However, disparities in probability distributions and the deterioration of performance ...

One-dimensional Path Convolution Linearly scaling 1D convolution provides parameter efficiency, but its naive integration into CNNs disrupts image locality, thereby degrading performance. We present ...

HDVAE : identifying spatial domains in spatial transcriptomics data with Hierarchical Decoupled Variational Autoencoder Overview of HDVAE In this study, we proposed HDVAE (Hierarchical Decoupled ...

Therefore, they designed an Effective Channel Attention (ECA) (Wang et al., 2020) by replacing the fully connected (FC) layer in SE with one-dimensional convolution with adaptive kernel sizes. ( Woo ...

Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been ...

HEARTBEAT CLASSIFICATION ALGORITHM BASED ON ONE-DIMENSIONAL CONVOLUTION NEURAL NETWORK. ... An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural ...

The encoder contains four one-dimensional convolution layers, each with a kernel of 7 points and 64 channels, interleaved with three ReLU activations. ... As detailed in Section 2.3, the ShimNet ...