On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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7 results for “Density-aware”

Uncertainty Estimation by Density Aware Evidential Deep Learningcs.LG

DAEDL improves EDL's OOD detection and classification performance by integrating feature space density.

problem Limited OOD detection and classification performance of EDL.

method Integrates feature space density with EDL's output and uses a novel parameterization.

result Demonstrates state-of-the-art performance across uncertainty estimation and classification tasks.

uncertainty estimation density aware evidential deep learning out-of-distribution detection classification

2024-09-13abs ↗pdf ↗

Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtrationmath.ST

A new method detects small holes in noisy data.

problem Detecting small holes in high-density regions from noise.

method Robust Density-Aware Distance (RDAD) filtration, incorporating distance-to-measure concept.

result The RDAD filtration prolongs the persistences of small holes, making them distinguishable from noise.

topological data analysis density-aware distance small hole detection robustness density weighting

2022-04-16abs ↗pdf ↗

On Learning Density Aware Embeddingscs.LG

A new deep metric learning method pulls embeddings towards dense clusters to improve classification accuracy.

problem Improving classification accuracy in deep metric learning models.

method Density Aware Metric Learning (DAML) which pulls embeddings towards the densest regions of clusters for each class.

result DAML achieves faster convergence and higher generalizability compared to existing methods.

deep metric learning density aware cluster center noise tolerance cross-modal face recognition

2019-04-08abs ↗pdf ↗

Probability-density-aware Semi-supervised Learningstat.ML

This paper introduces PM and PMLP to enhance SSL by considering probability density and cluster assumptions.

problem Insufficient utilization of unlabeled data in SSL.

method Introduces PM to discern similarity and PMLP to consider cluster assumption in label propagation.

result PMLP outperforms other methods in SSL tasks.

Semi-supervised learning Probability density Cluster assumption Label propagation Pseudo-labeling

2024-12-23abs ↗pdf ↗

Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classificationcs.CV

Two active learning algorithms improve HSI classification using Fermat distances and harmonic label propagation.

problem Semi-supervised hyperspectral image classification with limited labeled data.

method Combines Fermat distances with Poisson-reweighted harmonic label propagation for active point selection.

result FALL and A-FALL algorithms enhance labeling accuracy and scalability for large HSI scenes.

Hyperspectral imaging Active learning Density-aware Fermat distances Harmonic label propagation

2026-08-03abs ↗pdf ↗

Conformal-DP: A Density-Aware Mechanism for Differential Privacy over Riemannian Manifolds via Conformal Transformationcs.CR

Conformal-DP improves differential privacy on manifold data by calibrating perturbations based on local densities.

problem Lack of density-awareness in existing differential privacy mechanisms for manifold data leads to biased and suboptimal privacy-utility trade-offs.

method Proposes Conformal-DP, a density-aware differential privacy mechanism using conformal transformations to calibrate perturbations based on local densities.

result Demonstrates improved privacy-utility trade-off in heterogeneous data distribution settings compared to state-of-the-art mechanisms.

Differential Privacy Riemannian Manifolds Density-Aware Conformal Transformations Privacy Utility

2025-04-29abs ↗pdf ↗

Differentially Private Nonparametric Modal Learning with Applications to Regression and Clusteringmath.ST

Private estimation of density modes for multimodal distributions.

problem Estimating density modes under differential privacy constraints.

method DP-GRAMS, a mean-shift inspired method that performs noisy ascent on a differentially private score estimator.

result Private recovery of density modes with high probability and asymptotic error rates.

differential privacy nonparametric mode estimation multimodal distributions density modes mean-shift

2026-07-31abs ↗pdf ↗