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.
Adam: A method for stochastic optimization
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RGT-Est transforms relative geologic time estimation into a sinusoidal space and applies pointwise, perceptual, and adversarial losses to achieve better stratigraphic consistency and horizon correlation on seismic data.
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.
LPLCv2 is a larger, more annotated dataset for fine-grained license plate legibility classification with a baseline model reaching 89.5% F1-score via a new training method and camera-contamination protocol.
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
citing papers explorer
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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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Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping
RGT-Est transforms relative geologic time estimation into a sinusoidal space and applies pointwise, perceptual, and adversarial losses to achieve better stratigraphic consistency and horizon correlation on seismic data.
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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.
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LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification
LPLCv2 is a larger, more annotated dataset for fine-grained license plate legibility classification with a baseline model reaching 89.5% F1-score via a new training method and camera-contamination protocol.
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Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.