Results 141 to 150 of about 14,957 (259)
Operator-level quantum acceleration of non-logconcave sampling. [PDF]
Leng J, Ding Z, Chen Z, Lin L.
europepmc +1 more source
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
A Review: Construction of Statistical Distributions. [PDF]
Fang KT, Lin YX, Deng YH.
europepmc +1 more source
Statistical properties of the generalized inverse Gaussian distribution
openaire +1 more source
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Robust Pose and Inertial Parameter Estimation of an Unknown Aircraft Based on Variational Bayesian Dual Vector Quaternion Extended Kalman Filter. [PDF]
Xu S, Fang Y, Huang H.
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Variational adaptive Gaussian approximation filter for nonlinear systems with generalized unknown disturbances. [PDF]
Qin Y, Lv J, Li S, Hou Y.
europepmc +1 more source
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi +3 more
wiley +1 more source
Failure Lifetime Evaluation Based on Accelerated Generalized Wiener Degradation Process Models with Random Diffusion Coefficients. [PDF]
Li S, Yan Z.
europepmc +1 more source

