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We propose a novel autoencoder framework for FTN transmission to jointly optimize the ISI caused by FTN signaling and the impairments of the physical channel. The feasibility of the framework is ...
To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...
Bai et al. [54] proposed an unsupervised autoencoder framework that uses Random Fourier Feature embeddings for clustering modulation signals. Combined with a novel separable loss function, their model ...
First latent diffusion autoencoder framework for 3D medical images - LDAE learns semantic representations without supervision, suitable for classification, manipulation, and interpolation of brain ...
In this viewpoint, we briefly review recently developed autoencoder-based models designed to enhance the conformational exploration of IDPs through embedding and latent sampling.
Recently, the autoencoder (AE) has received significant attention in the hyperspectral anomaly detection task. However, all existing AE-based anomaly detectors operate under the linear mixing model ...
Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and ...