Results 181 to 190 of about 13,423,347 (294)

Robust graph structure learning to improve multi-omics cancer subtype classification. [PDF]

BMC Bioinformatics
Guo M, Ye X, Sakurai T.
europepmc   +1 more source

Nonparametric Causal Structure Learning in High Dimensions. [PDF]

Entropy (Basel), 2022
Chakraborty S, Shojaie A.
europepmc   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga   +2 more
wiley   +1 more source

CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph structure learning. [PDF]

PLoS Comput Biol
Zhang X, Zou Q, Wang C, Niu M.
europepmc   +1 more source

Generic Structure Extraction with Bi-Level Optimization for Graph Structure Learning. [PDF]

Entropy (Basel), 2022
Yin N, Luo Z.
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Causal Structure Learning Assumptions Shape Counterfactual Safety: Expert-Guided Constraints vs. Data-Driven DAGs with Probabilistic Logic Twin Networks. [PDF]

Entropy (Basel)
Avilés H, Gracia I, Kiesel R, Rodríguez V, Machucho R, Reyes A, Negrete M, Ramírez G, Luévano N, Pequeño M, Medrano J, Weitkämper F.   +11 more
europepmc   +1 more source

Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models. [PDF]

Stat Anal Data Min, 2022
Mulgrave JJ, Ghosal S.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Comparative evaluation of score criteria for dynamic Bayesian Network structure learning. [PDF]

PLoS One
Yaman A, Cengiz MA.
europepmc   +1 more source