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20202026
most citedCascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

17 citations · 39 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction

Jinghao Cao, Minsung Kang, Hongyue Sun +5

Predicting droplet evolution in material jetting, or Inkjet Printing (IJP), is essential for maintaining printing quality. However, long-horizon forecasts remain challenging due to…

cs.LG2023

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems

Jihoon Chung, Zhenyu Kong

Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue c…

cs.LG2023

On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee

Chenyang Li, Jihoon Chung, Mengnan Du +3

Model compression is a crucial part of deploying neural networks (NNs), especially when the memory and storage of computing devices are limited in many applications. This paper foc…

cs.LG2022★ 3 cited

Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network

Jihoon Chung, Bo Shen, Zhenyu +1

Supervised classification methods have been widely utilized for the quality assurance of the advanced manufacturing process, such as additive manufacturing (AM) for anomaly (defect…

cs.LG2022★ 4 cited

Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing

Jihoon Chung, Bo Shen, Andrew Chung Chee Law +2

Additive Manufacturing (AM) is a powerful technology that produces complex 3D geometries using various materials in a layer-by-layer fashion. However, quality assurance is the main…

cs.LG2022★ 14 cited

Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks

Raghav Gnanasambandam, Bo Shen, Jihoon Chung +3

Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weath…