211 citations · 969 across the 37 of their papers we have counts for
10 papers · 1 filter
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…
Galaxy Deblending using Residual Dense Neural networks
Hong Wang, Sreevarsha Sreejith, Anže Slosar +2
We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic…
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,…
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning
Hong Wang, Yuefan Deng, Shinjae Yoo +2
While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully designed adversarial attacks. We propose a novel adversarial training-base…
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
Patrick R. Johnstone, Jonathan Eckstein, Thomas Flynn +1
We present a new, stochastic variant of the projective splitting (PS) family of algorithms for monotone inclusion problems. It can solve min-max and noncooperative game formulation…
Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan +20
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In…
Federated Quantum Machine Learning
Samuel Yen-Chi Chen, Shinjae Yoo
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially impr…
Quantum machine learning with differential privacy
William M Watkins, Samuel Yen-Chi Chen, Shinjae Yoo
Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. A…
Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networks
Longlong Wu, Shinjae Yoo, Ana F. Suzana +5
As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological inform…
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…