A shape-aware loss strategy recovers sub-threshold S-wave arrivals in deep learning seismic phase pickers by treating labels as coherent shapes, achieving a 64% increase in effective detections.
Alternating back-propagation for generator network
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Scaling noise magnitude in NCE aligns gradients with MLE, enabling a practical approximation that improves performance on CIFAR-10 and ImageNet image modeling with fewer training steps.
Visual generation models are evolving from passive renderers to interactive agentic world modelers, but current systems lack spatial reasoning, temporal consistency, and causal understanding, with evaluations overemphasizing perceptual quality.
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Recovering Sub-threshold S-wave Arrivals in Deep Learning Phase Pickers via Shape-Aware Loss
A shape-aware loss strategy recovers sub-threshold S-wave arrivals in deep learning seismic phase pickers by treating labels as coherent shapes, achieving a 64% increase in effective detections.
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"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Scaling noise magnitude in NCE aligns gradients with MLE, enabling a practical approximation that improves performance on CIFAR-10 and ImageNet image modeling with fewer training steps.
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Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling
Visual generation models are evolving from passive renderers to interactive agentic world modelers, but current systems lack spatial reasoning, temporal consistency, and causal understanding, with evaluations overemphasizing perceptual quality.
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