Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

A gradual fine-tuning strategy was employed, progressively unfreezing the last 6 layers of ESM-2 encoder and applying discriminative learning rates with the AdamW optimizer. The model was trained ...

Healthcare question answering (HQA) system plays a vital role in encouraging patients to inquire for professional consultation. However, there are some challenging factors in learning and representing ...

Figure 1. The structural flowchart of the method proposed in this paper. 3. Stacked Convolutional Autoencoder with Fusion Selection Kernel Attention Mechanism 3.1. Construction of Network Structure In ...

A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...

3.1 2D convolutional autoencoder An autoencoder (AE) is a neural network model primarily used for unsupervised learning. It achieves dimensionality reduction and feature extraction by learning to ...

Before delving into their evolution, it's essential to understand what autoencoders are and how they function. An autoencoder is a type of neural network that consists of an encoder, which compresses ...

Depending on the specific soft sensing problem and data characteristics, suitable deep learning model architectures can be developed, including deep neural networks (DNN), convolutional neural ...

Cite GCA-ROM [1] Pichi, F., Moya, B. and Hesthaven, J.S. (2023) ‘A graph convolutional autoencoder approach to model order reduction for parametrized PDEs’. Available at: arXiv, Journal of ...