211 citations · 450 across the 27 of their papers we have counts for
6 papers · 1 filter
A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing
Xihaier Luo, Ahsan Kareem, Liting Yu +1
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant…
Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
Yi Huang, Yihui Ren, Shinjae Yoo +1
Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmo…
Feature Importance in a Deep Learning Climate Emulator
Wei Xu, Xihaier Luo, Yihui Ren +3
We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for "understanding" a deep learning (DL) emulator of climate. Specifically,…
Hybrid Quantum-Classical Graph Convolutional Network
Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang +2
The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techni…
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang +2
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from t…
Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
Kwangmin Yu, Thomas Flynn, Shinjae Yoo +1
Stochastic Gradient Descent (SGD) is the most popular algorithm for training deep neural networks (DNNs). As larger networks and datasets cause longer training times, training on d…