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.

168,742 papers · 148 categories

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5 results for “echocardiogram”

Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Modelscs.LG

Proposes a novel anomaly detection method for echocardiogram videos.

problem Anomaly detection in echocardiogram videos.

method Dynamic Variational Trajectory Models (TVAE-C, TVAE-R, TVAE-S) trained on healthy infant echocardiogram videos.

result Superior performance in detecting congenital heart defects and pulmonary hypertension.

echocardiogram anomaly detection heart defects variational autoencoders congenital heart disease

2022-06-30abs ↗pdf ↗

GAN-enhanced Conditional Echocardiogram Generationeess.IV

GANs improve echo frame generation with labeled patches.

problem Automated echocardiography with limited labels.

method Conditional GAN with patch-based discriminator.

result GAN-enhanced echo frames match given segmentation masks.

GAN echocardiography semi-supervised cardiac segmentation machine learning

2019-11-05abs ↗pdf ↗

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metricsq-bio.QM

Bayesian neural networks improve SHD classification and uncertainty quantification.

problem Improving screening for structural heart disease using noninvasive ECG and echocardiography.

method Comparing frequentist and Bayesian neural network classifiers on the EchoNext dataset.

result Bayesian classifiers provide more robust uncertainty quantification.

Bayesian inference structural heart disease machine learning electrocardiography echocardiography

2026-05-21abs ↗pdf ↗

Structural interpretability in SVMs with truncated orthogonal polynomial kernelsstat.ML

A method for interpreting SVMs using polynomial kernels, revealing model complexity.

problem Interpreting SVMs built with truncated orthogonal polynomial kernels.

method Orthogonal Representation Contribution Analysis (ORCA) with normalized Orthogonal Kernel Contribution (OKC) indices.

result The method reveals structural aspects of model complexity not captured by predictive accuracy.

Support Vector Machines Orthogonal Polynomial Kernels Interpretability Reproducing Kernel Hilbert Space Orthogonal Kernel Contribution

2026-04-16abs ↗pdf ↗

Importance Sparsification for Sinkhorn Algorithmstat.ML

A new method reduces the computational cost of Sinkhorn algorithm for OT and UOT problems.

problem High computational complexity of Sinkhorn algorithm for OT and UOT problems.

method Importance sparsification method called Spar-Sink to efficiently approximate entropy-regularized OT and UOT solutions.

result The method reduces computational cost from

O(n^2)

\widetilde{O}(n)

, and is consistent under mild regularity conditions.

Sinkhorn algorithm Optimal transport Unbalanced optimal transport Computational complexity Sparse kernel matrix

2023-06-11abs ↗pdf ↗