Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence.
A building self-shading shape impacts substantially on the amount of direct sunlight received by the building and contributes significantly to building operational energy use, in addition to other major contributing variables, such as materials and window-to-wall ratios.
We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos.
To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF).
User behavior and feature interactions are crucial in deep learning-based recommender systems.
Previous database systems extended their SQL dialect to support ML.
In this study, an object recognition problem is investigated to initially predict the label of unseen sample images based on training dataset consisting of different types of synthetic 2D shapes; later, a generative DL algorithm is applied to be trained and generate new shapes for given labels.
Point cloud is a fundamental 3D representation which is widely used in real world applications such as autonomous driving.