ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Alternating back-propagation for generator network
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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.
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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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.