Search Results for author:
Found 15 papers, 2 papers with code
SQLFlow: A Bridge between SQL and Machine Learning
Previous database systems extended their SQL dialect to support ML.
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF).
Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds
Point cloud is a fundamental 3D representation which is widely used in real world applications such as autonomous driving.
Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset
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.
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System
Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models.
BIM Hyperreality: Data Synthesis Using BIM and Hyperrealistic Rendering for Deep Learning
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.
Convolutional versus Dense Neural Networks: Comparing the Two Neural Networks Performance in Predicting Building Operational Energy Use Based on the Building Shape
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.
Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition
Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence.
Optimization simulation of reflow welding based on prediction of regional center temperature field
According to the heat conduction law and the specific heat capacity formula, the first-order ordinary differential equation of the central temperature curve of the welding area with respect to the temperature distribution function in the furnace on the conveyor belt displacement is obtained.
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition
We study the use of parametric building information modeling (BIM) to automatically generate training data for artificial neural networks (ANNs) to recognize building objects in photos.
DIME-Net: Neural Network-Based Dynamic Intrinsic Parameter Rectification for Cameras with Optical Image Stabilization System
Here we propose a novel neural network-based approach that estimates $\mathrm{K}$ matrix in real-time so that pose estimation or scene reconstruction can be run at camera native resolution for the highest accuracy on mobile devices.
Experiments on Generative AI-Powered Parametric Modeling and BIM for Architectural Design
By integrating ChatGPT for scripting and Veras for generating design ideas with widely used parametric modeling and BIM tools, the framework provides architects with an intuitive and powerful method to convey design intent, leading to more efficient, creative, and collaborative design processes.
Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions for Assembly
Thus, using fewer phases simplifies the complex assembly process while step count facilitates accurate object recognition and precise visualization of each step.
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i. e., graph data augmentation and attack.
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction
This research aims to extend the Semantic Neural Radiance Fields (Semantic-NeRF) model by focusing solely on semantic output and removing the RGB output component.
