Mean-field perturbation theory of dropout at the edge of chaos yields distinct universality classes for smooth versus kinked activations, critical scaling laws for correlation decay, and front-loaded dropout schedules that reduce test loss.
International Conference on Learning Representations , year =
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CHASM introduces a cross-frequency harmonized axis-separable spectral mixer using a shared channel eigenbasis plus per-frequency positive gains, yielding consistent gains over same-backbone baselines in medical and natural image tasks.
Spectrum-adaptive post-hoc generalization bounds for multi-layer Transformers are derived using layerwise Schatten quantities whose indices are chosen after training based on singular-value profiles.
Viewpoint-conditioned feature selection improves thermal vehicle re-identification mAP by 19.7% on RGBNT100 and 12.8% on a new maritime dataset by adapting RGB ViT extractors.
Setting β in balanced Adam to achieve a refresh count R_β ≈1000 based on effective learning horizon T_ES improves validation robustness over fixed-β baselines across 11 vision and language experiments.
citing papers explorer
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Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
Mean-field perturbation theory of dropout at the edge of chaos yields distinct universality classes for smooth versus kinked activations, critical scaling laws for correlation decay, and front-loaded dropout schedules that reduce test loss.
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CHASM: Cross-frequency Harmonized Axis-Separable Mixing for Spectral Token Operators
CHASM introduces a cross-frequency harmonized axis-separable spectral mixer using a shared channel eigenbasis plus per-frequency positive gains, yielding consistent gains over same-backbone baselines in medical and natural image tasks.
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Spectrum-Adaptive Generalization Bounds for Trained Deep Transformers
Spectrum-adaptive post-hoc generalization bounds for multi-layer Transformers are derived using layerwise Schatten quantities whose indices are chosen after training based on singular-value profiles.
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VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Vision
Viewpoint-conditioned feature selection improves thermal vehicle re-identification mAP by 19.7% on RGBNT100 and 12.8% on a new maritime dataset by adapting RGB ViT extractors.
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Refresh-Scaling the Memory of Balanced Adam
Setting β in balanced Adam to achieve a refresh count R_β ≈1000 based on effective learning horizon T_ES improves validation robustness over fixed-β baselines across 11 vision and language experiments.