EVA-0 is a zeroth-order test-time adaptation method that uses scale-invariant loss, anchor-guided optimization, and symmetric two-sided perturbations to enable inference and adaptation in two forward passes, outperforming prior methods on ImageNet-C with ViT-Base.
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MC-RFM achieves superior few-shot adaptation by representing features on a mixed hyperbolic-Euclidean manifold and learning task-conditioned continuous transport via Riemannian flow matching to hybrid prototypes.
Models multi-head transformer data flow as time-dependent Wasserstein gradient flows of an attention-capturing interaction energy, with proofs on omega-limit stationary points and stability under weight and input perturbations.
citing papers explorer
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EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
EVA-0 is a zeroth-order test-time adaptation method that uses scale-invariant loss, anchor-guided optimization, and symmetric two-sided perturbations to enable inference and adaptation in two forward passes, outperforming prior methods on ImageNet-C with ViT-Base.
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MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching
MC-RFM achieves superior few-shot adaptation by representing features on a mixed hyperbolic-Euclidean manifold and learning task-conditioned continuous transport via Riemannian flow matching to hybrid prototypes.
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Multi-Headed Transformer Architectures as Time-dependent Wasserstein Gradient Flows
Models multi-head transformer data flow as time-dependent Wasserstein gradient flows of an attention-capturing interaction energy, with proofs on omega-limit stationary points and stability under weight and input perturbations.