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 ...

The autoencoder is an unsupervised deep neural network that learns a compressed representation from the input data and reconstructs an output that is as similar as possible to the original data.

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, neuro-transfer function (neuro-TF) has become a recognized method for electromagnetic (EM) parametric modeling. The existing neuro-TF methods use the vector fitting technique to perform ...

Tranformer-based Denoising AutoEncoder for Sentence Transformers Unsupervised pre-training The acquisition of sentence embeddings often necessitates a substantial volume of labeled data. However, in ...

Autoencoder for Product Matching This was an experiment for a possible PhD topic. The main idea was to use different Autoencoder for entity resolution / product matching. The core idea was to pretrain ...

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such ...