Machine Learning articles for The University of Hong Kong (HKU), China

Time frame: 1 June 2025 - 30 May 2026

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Count: 18

Article ‘Count’ for Machine Learning.

Journal Count Share
1 1.00
Privacy-preserving probabilistic wind power forecasting: An adaptive federated approach 1.00
1 0.75
Large-scale building dilapidation assessment for high-density cities: An urban visual intelligence approach 0.75
5 2.87
Hybrid Transformer-Mamba Model for 3D Semantic Segmentation 0.25
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks 0.45
Mesh-Learner: Texturing Mesh with Spherical Harmonics 1.00
GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction 1.00
AnyTSR: Any-Scale Thermal Super-Resolution for UAV 0.17
7 3.47
Trustworthy tree-based machine learning by MoS2 flash-based analog content-addressable memory with inherent soft boundaries 0.70
Ultrafast visual perception beyond human capabilities enabled by motion analysis using synaptic transistors 0.02
Pruning random resistive memory for optimizing analog AI 0.43
Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness 0.50
Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition 0.07
Memristor-based adaptive analog-to-digital conversion for efficient and accurate compute-in-memory 0.75
ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling 1.00
1 0.11
Automaticity speeds the retrieval of instances from the human hippocampus 0.11
2 0.45
Privacy-preserving data analysis using a memristor chip with colocated authentication and processing 0.44
Memristive floating-point Fourier neural operator network for efficient scientific modeling 0.01
1 1.00
Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments 1.00

Numerical information only (in the tables above) is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.