Posital has enhanced its IXARC family of incremental magnetic rotary encoders, increasing the maximum resolution to 32,768 pulses per revolution (PPR). This upgrade also includes new phasing options, ...

Uzma,, Manzoor, U. and Halim, Z. (2023) Protein Encoder An Autoencoder-Based Ensemble Feature Selection Scheme to Predict Protein Secondary Structure. Expert Systems with Applications, 213, Article ...

Rapid advances in single-cell RNA sequencing (scRNA-seq) have made it possible to characterize cell states at a high resolution view for large scale library. scRNA-seq data contains a great deal of ...

Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural network’s latent representations into sparse, seemingly interpretable features. While these models have ...

Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...

Sparse autoencoders (SAE) use the concept of autoencoder with a slight modification. During the encoding phase, the SAE is forced to only activate a small number of the neurons in the intermediate ...

Each thin blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

This image illustrates an autoencoder neural network architecture, focusing on the hidden layer role in transforming data into an embedding vector format. It highlights the encoder compression ...

Each small blue arrow represents a neural weight, which is just a number, typically between about -2 and +2. Weights are sometimes called trainable parameters. The small red arrows are special weights ...

Generic Deep Autoencoder for Time-Series This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder ...

As shown in Figure 1, the autoencoder has a symmetric structure consisting of two components: an encoder and a decoder (Adem et al., 2019). The encoder contracts a nonlinear mapping between the input ...