Results 61 to 70 of about 32,144 (182)

Augmented Sliced Wasserstein Distances

, 2020
While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accuracy if the number of projections ...
Chen, Xiongjie, Yang, Yongxin, Li, Yunpeng   +2 more
openaire   +2 more sources

IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Advanced Intelligent Systems, EarlyView.
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi, Olatunji Omisore, Wenjing Du, Wenke Duan, Chen Bailang, Liu Kun, Lei Wang, Minxin Wei   +7 more
wiley   +1 more source

Supervised Tree-Wasserstein Distance

, 2021
To measure the similarity of documents, the Wasserstein distance is a powerful tool, but it requires a high computational cost. Recently, for fast computation of the Wasserstein distance, methods for approximating the Wasserstein distance using a tree metric have been proposed.
Takezawa, Yuki, Sato, Ryoma, Yamada, Makoto   +2 more
openaire   +2 more sources

Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering

Data Science in Finance and Economics
Recent work has proposed Wasserstein k-means (Wk-means) clustering as a powerful method to classify regimes in time series data, and one-dimensional asset returns in particular.
Qinmeng Luan, James Hamp
doaj   +1 more source

Survey of Distances between the Most Popular Distributions

Analytics, 2023
We present a number of upper and lower bounds for the total variation distances between the most popular probability distributions. In particular, some estimates of the total variation distances in the cases of multivariate Gaussian distributions ...
Mark Kelbert
doaj   +1 more source

Geometrical Insights for Implicit Generative Modeling

, 2019
Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean ...
A Auffinger, A Gretton, A Müller, AA Zinger, B Schölkopf, B Sriperumbudur, BK Sriperumbudur, BK Sriperumbudur, C Villani, D Sejdinovic, GJ Székely, H Cramér, IJ Schoenberg, JM Hammersley, MA Aizerman, N Aronszajn, N Fournier, P Milgrom, R Mises von, RJ Serfling, RM Neal, ST Rachev, Steffen Dereich, T Hastie, VS Borkar, X Nguyen   +25 more
core   +1 more source

Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

Advanced Intelligent Systems, EarlyView.
Cardiovascular diseases are leading death causes; electrocardiogram (ECG) analysis is slow, motivating machine learning and deep learning. This study compares deep convolutional generative adversarial network, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP) for synthetic ECG spectrograms; Fréchet Inception Distance (FID) and ...
Giovanny Barbosa‐Casanova, Norelli Schettini, Winston Percybrooks, Begoña García‐Zapirain   +3 more
wiley   +1 more source

How can welfare regime and production regime theories explain differences in schools’ ability grouping policies? A comparative study using the PISA school survey

British Educational Research Journal, EarlyView.
Abstract Research evidence is mixed on the consequences of ability grouping policies, but most research has found an overrepresentation of disadvantaged social demographics in low‐ability groups. However, researchers have neglected to explain why ability grouping policies vary between countries.
Monica Reichenberg, Girma Berhanu, Lena Fridlund   +2 more
wiley   +1 more source

Full-Waveform Inversion of Two-Parameter Ground-Penetrating Radar Based on Quadratic Wasserstein Distance

Remote Sensing
Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal ...
Kai Lu, Yibo Wang, Heting Han, Shichao Zhong, Yikang Zheng   +4 more
doaj   +1 more source

Hyperspectral Anomaly Detection Based on Wasserstein Distance and Spatial Filtering

Remote Sensing, 2022
Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD ...
Xiaoyu Cheng, Maoxing Wen, Cong Gao, Yueming Wang   +3 more
doaj   +1 more source