Results 161 to 170 of about 3,020,093 (290)

Robust Unsupervised Domain Adaptation from A Corrupted Source. [PDF]

Proc IEEE Int Conf Data Min, 2022
Yu S, Zhu Z, Liu B, Jain AK, Zhou J.
europepmc   +1 more source

Learning regime‐dependent governing equations: A symbolic decision tree approach

AIChE Journal, EarlyView.
Abstract Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data‐driven discovery of regime‐dependent governing equations essential for predictive modeling, optimization, and control. We propose symbolic decision trees for the data‐driven discovery of regime‐dependent governing equations.
Ilias Mitrai, Tongjia Liu, Gabriel E. Sanoja   +2 more
wiley   +1 more source

A curriculum-guided unified framework for robust unsupervised domain adaptation on multi-cohort Parkinson's disease diagnosis. [PDF]

Quant Imaging Med Surg
Shen Y, Hai J, Qiao K, Chen J, Li Y, Yan B.   +5 more
europepmc   +1 more source

Overcoming field variability: unsupervised domain adaptation for enhanced crop-weed recognition in diverse farmlands. [PDF]

Front Plant Sci, 2023
Ilyas T, Lee J, Won O, Jeong Y, Kim H.
europepmc   +1 more source

Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological ...
Kihara, D, Kihara, D., Xu, Min, Kihara, Daisuke, Li, H, Du, B, Shi, J, Xu, M, Shi, J., Li, Xingjian, Du, Bo, Chen, H, Shi, Jiahua, Chen, Huaming, Du, B., Shen, Jun, Shen, J, Barthelemy, Johan, Li, X., Barthelemy, J., Shen, J., Li, Haoran, Li, X, Chen, H., Li, H., Xu, M., Barthelemy, J   +26 more
core   +1 more source

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

Improving Generalization of Deep Learning for Glaucoma Classification Under Real-World Domain Shift via Unsupervised Domain Adaptation. [PDF]

Transl Vis Sci Technol
Rashidisabet H, Chan RVP, Vajaranant TS, Yi D.   +3 more
europepmc   +1 more source

A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation. [PDF]

Bioengineering (Basel), 2023
Ananda S, Jain RK, Li Y, Iwamoto Y, Han XH, Kanasaki S, Hu H, Chen YW.   +7 more
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Unsupervised Domain Adaptation Algorithm for Time Series Based on Adaptive Contrastive Learning. [PDF]

Entropy (Basel)
Liu H, Lin P.
europepmc   +1 more source