Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.
Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Affinity-propagation clustering of Arctic VHSR imagery enables MAE pretraining of a ViT-Large encoder that outperforms ImageNet and Prithvi-EO-2.0 baselines by 5-15 percentage points in mean F1 on four downstream Arctic detection and segmentation tasks.
citing papers explorer
-
Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm
Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.
-
Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing
Affinity-propagation clustering of Arctic VHSR imagery enables MAE pretraining of a ViT-Large encoder that outperforms ImageNet and Prithvi-EO-2.0 baselines by 5-15 percentage points in mean F1 on four downstream Arctic detection and segmentation tasks.