PhyloSDF generates novel 3D skull morphologies for Darwin's finches via phylogenetically-conditioned residual flow matching, achieving 88-129% of real intra-species variation from few specimens and enabling phylogenetic extrapolation.
Understanding deep learning (still) requires rethinking generalization,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces integration, metastability, and dynamical stability index measures from layer activations and reports patterns distinguishing CIFAR-10 from CIFAR-100 difficulty plus early convergence signals across ResNet variants, DenseNet, MobileNetV2, VGG-16, and a Vision Transformer.
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PhyloSDF: Phylogenetically-Conditioned Neural Generation of 3D Skull Morphology via Residual Flow Matching
PhyloSDF generates novel 3D skull morphologies for Darwin's finches via phylogenetically-conditioned residual flow matching, achieving 88-129% of real intra-species variation from few specimens and enabling phylogenetic extrapolation.
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Training Deep Visual Networks Beyond Loss and Accuracy Through a Dynamical Systems Approach
Introduces integration, metastability, and dynamical stability index measures from layer activations and reports patterns distinguishing CIFAR-10 from CIFAR-100 difficulty plus early convergence signals across ResNet variants, DenseNet, MobileNetV2, VGG-16, and a Vision Transformer.