WILD-SAM is a fine-tuned SAM variant using phase-aware MoE adapters and wavelet subband enhancement that achieves state-of-the-art landslide detection on wrapped InSAR data.
Dselect-k: Differentiable selection in the mixture of experts with applications to multi-task learning,
2 Pith papers cite this work. Polarity classification is still indexing.
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UMDA combines multi-objective learning with uncertainty modeling for RTA interception and applies distillation to enable single-pass aleatoric plus epistemic uncertainty with 10x inference speedup on JD and Criteo data.
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WILD-SAM: Phase-Aware Expert Adaptation of SAM for Landslide Detection in Wrapped InSAR Interferograms
WILD-SAM is a fine-tuned SAM variant using phase-aware MoE adapters and wavelet subband enhancement that achieves state-of-the-art landslide detection on wrapped InSAR data.
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Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration
UMDA combines multi-objective learning with uncertainty modeling for RTA interception and applies distillation to enable single-pass aleatoric plus epistemic uncertainty with 10x inference speedup on JD and Criteo data.