Encoder-Decoder Architectures. Encoder-decoder architectures are a broad category of models used primarily for tasks that involve transforming input data into output data of a different form or ...

(2022, December 12-16). Quantile-Based Encoder-Decoder Deep Learning Models for Multi-Step Ahead Hydrological Forecasting [Conference presentation]. American Geophysical Union (AGU) Fall Meeting 2022, ...

Since the deep learning boom has started, numerous researchers have started building many architectures around neural networks. It is often speculated that the neural networks are inspired by neurons ...

We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on the assumption that the image available to the ...

Due to its use in a variety of fields, including autonomous driving, robot navigation, remote sensing, medical research, agriculture etc.,In recent years, video scene parsing (VSP) has become ...

Abstract: This study introduces an innovative deep learning framework, the Weber Cross Information Sharing Deep Learning Encoder-Decoder (WCISD-ED) model, designed for emotion recognition through ...

Keywords: ligand binding sites, drug discovery and development, in silico drug design, deep learning, graph neural network, recurrent neural network, generative model, machine learning. Citation: Shi ...