Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
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Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
A 10-qubit convolutional quantum graph neural network fed by autoencoder-compressed jet data achieves performance comparable to classical graph networks in distinguishing boosted Z jets from gluon jets.
A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.
Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
PuzzleMark provides a robust and imperceptible watermarking method for code datasets using adaptive variable name concatenation and statistical verification, achieving perfect detection rates with minimal performance impact.
A hierarchical prior-decoder model using CLIP latents generates more diverse text-conditional images than direct methods while preserving photorealism and caption fidelity.
Introduces Hell-Char, PaLit-Char and Med-Char datasets plus a similarity-weighted supervised contrastive loss and lacuna augmentation to learn diachronic embeddings for ancient Greek letterforms.
EMSL groups material points into clusters, samples a reference strain per cluster once per increment, and computes a linearised stress estimate from the reference tangent and POD strain modes, yielding an affine reduced system that requires no iterations online and Pareto-dominates prior strain-cubc
A broad empirical benchmark shows how 15 existing test selection metrics perform for fault detection, performance estimation, and retraining under corrupted, adversarial, temporal, natural, and label shifts across image, text, and Android data.
LQE is a physics-constrained learnable dimensionality reduction technique that improves average mIoU in hyperspectral urban segmentation on three datasets while using only 12-36 parameters.
A new GPU-oriented batch SVD solver based on the one-sided Jacobi method delivers significant speedups over vendor libraries and prior open-source implementations across precisions and matrix shapes.
Benchmark of Affine, AIM, JetFormer and VQ-VAE tokenizers on galaxy images shows decoupled reconstruction and representation performance with no consistent winner.
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
EZR.py shows that a compact, readable Python toolkit can match or exceed state-of-the-art tools like SHAP, LIME, SMAC3, and FASTREAD on over 120 tabular SE tasks while running 500 times faster and using far less labeled data.
The effectiveness of dimensionality reduction before clustering depends on matching the specific technique and target dimension count to the data geometry and the clustering algorithm used.
Machine learning methods are explored for pulse classification, artifact rejection, and shape analysis in metallic magnetic calorimeters to improve scalability over traditional signal processing.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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Hierarchical Text-Conditional Image Generation with CLIP Latents
A hierarchical prior-decoder model using CLIP latents generates more diverse text-conditional images than direct methods while preserving photorealism and caption fidelity.