Results 81 to 90 of about 38,801 (275)

Improving the Finite Sample Estimation of Average Treatment Effects Using Double/Debiased Machine Learning With Propensity Score Calibration

Journal of Applied Econometrics, EarlyView.
ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates.
Daniele Ballinari, Nora Bearth
wiley   +1 more source

Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning

IEEE Access
Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly.
Haiou Qin
doaj   +1 more source

The undersampled discrete Gabor transform

IEEE Transactions on Signal Processing, 1998
Conventional studies on discrete Gabor transforms have generally been confined to the cases of critical sampling and oversampling in which the Gabor families span the whole signal space. In this paper, we investigate undersampled discrete Gabor transforms. For an undersampled Gabor triple (g,a,b), i.e.
openaire   +1 more source

Benchmarking Hybrid CNN‐Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129Xe MRI Reconstruction

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Hyperpolarized 129Xe MRI faces technical challenges including low signal‐to‐noise ratio and breath‐hold constraints. Current literature focuses on proprietary deep learning methods or image‐domain enhancements. Purpose To present a comprehensive evaluation of transformer and hybrid CNN‐transformer architectures integrating dual ...
Ramtin Babaeipour, Matthew S. Fox, Grace Parraga, Alexei Ouriadov   +3 more
wiley   +1 more source

A MR Fingerprinting Development Kit for Quantitative 3D Brain Imaging

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Magnetic resonance fingerprinting (MRF) is an emerging quantitative imaging technique that enables multiparametric tissue characterization, but its adoption has been hindered by the complexity of data acquisition and post‐processing. These technical and implementation challenges have limited its broader clinical deployment.
Rasim Boyacioglu, Thomas Kluge, Guido Buonincontri, Wei‐Ching Lo, Stephan Kannengiesser, Mathias Nittka, Arashdeep Kaur, Andrew Dupuis, Dan Ma, Chaitra Badve, Mark A. Griswold, Yong Chen   +11 more
wiley   +1 more source

EKMGS: A HYBRID CLASS BALANCING METHOD FOR MEDICAL DATA PROCESSING

Scientific Journal of Astana IT University
The field of medicine is witnessing rapid development of AI, highlighting the importance of proper data processing. However, when working with medical data, there is a problem of class imbalance, where the amount of data about healthy patients ...
Zholdas Buribayev, Saida Shaikalamova, Ainur Yerkos, Rustem Imanbek   +3 more
doaj   +1 more source

A Spatio-Temporal Diffusion Model for Cardiac Real-Time Imaging. [PDF]

Magn Reson Med
ABSTRACT Purpose Real‐time imaging of cardiac function is favorable due to shorter scan times and becomes necessary when arrhythmia or inability to hold breath leads to insufficient quality of electrocardiogram (ECG)‐gated Cartesian cine. However, comparable spatio‐temporal resolution can only be achieved in undersampled settings, which in turn demand ...
Schad O, Heidenreich JF, Petri N, Hartung V, Kollmann A, Bley TA, Nordbeck P, Petritsch B, Wech T.   +8 more
europepmc   +2 more sources

Reviving Undersampling for Long-Tailed Learning

Pattern Recognition
The training datasets used in long-tailed recognition are extremely unbalanced, resulting in significant variation in per-class accuracy across categories. Prior works mostly used average accuracy to evaluate their algorithms, which easily ignores those worst-performing categories.
Hao Yu 0027, Yingxiao Du, Jianxin Wu 0001   +2 more
openaire   +2 more sources

Trainable Undersampling for Class-Imbalance Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most existing undersampling methods is that their data sampling strategies are heuristic-based and independent of the used classifier and evaluation metric. Thus, they may discard informative instances for the classifier during the data sampling.
Minlong Peng, Qi Zhang 0001, Xiaoyu Xing, Tao Gui, Xuanjing Huang 0001, Yu-Gang Jiang 0001, Keyu Ding, Zhigang Chen   +7 more
openaire   +2 more sources

Performance of a glider‐mounted multifrequency echosounder for measuring the vertical distribution and abundance of large pelagic copepods

Limnology and Oceanography: Methods, EarlyView.
Abstract The difficulty of sampling zooplankton communities in situ has driven advancements in autonomous, remote sensing technology. The goal of this paper was to perform a gear comparison study testing the performance of one such piece of technology—a glider‐mounted four‐frequency echosounder—against traditional shipboard methods of measuring ...
Delphine Mossman, Joseph D. Warren, Natasha J. Hynes, Kimberley T. A. Davies   +3 more
wiley   +1 more source