Results 51 to 60 of about 9,690 (264)

Quantifying uncertainty of machine learning methods for loss given default

Frontiers in Applied Mathematics and Statistics, 2022
Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting credit risk parameters, the approaches have been found to outperform standard statistical models. The quantification of prediction uncertainty is
Matthias Nagl, Maximilian Nagl, Daniel Rösch   +2 more
doaj   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

CLRe: A Synergistic Dual‐Engine Framework for One‐Step Retrosynthesis Prediction

Advanced Science, EarlyView.
CLRe uses a contrastive difficulty score to order pretrained seq2seq fine‐tuning for retrosynthesis. Reaction embeddings define the ranking score, and a cumulative easy‐to‐hard schedule expands from the easiest subset to the full training set while earlier examples remain active.
Tianhao Su, Xitao Wang, Musen Li, Guanhua Qin, Shunbo Hu, Tong‐Yi Zhang   +5 more
wiley   +1 more source

Inverse Design of Pre‐Strain via a Predictive Electromechanical Model for Enhanced Strain Sensing

Advanced Science, EarlyView.
An analytical electromechanical model rooted in microcrack evolution mechanics is globally calibrated from only eight pre‐strain datasets, enabling prediction of the complete sensing response at untested pre‐strain values within the calibrated design space (R2 > 0.99).
Jia‐Chen Shang, Rui Wang, Bo Lu, Ming‐Liang Song, Yu‐Yi Chen, Kuo Han, Xin‐Jie Feng, Bin Sun, Yao‐Guang Cao, Xiao‐Yu Yan, Shi‐Chun Yang   +10 more
wiley   +1 more source

Interval-Valued Uncertainty Based on Entropy and Dempster–Shafer Theory

Journal of Statistical Theory and Applications (JSTA), 2018
This paper presents a new structure as a simple method at two uncertainties (i.e., aleatory and epistemic) that result from variabilities inherent in nature and a lack of knowledge.
F. Khalaj, E. Pasha, R. Tavakkoli-Moghaddam, M. Khalaj   +3 more
doaj   +1 more source

Epistemic Uncertainty Propagation in Power Models

Electronic Notes in Theoretical Computer Science, 2018
Abstract Data-centers have recently experienced a fast growth in energy demand, mainly due to cloud computing, a paradigm that lets the users access shared computing resources (e.g., servers, storage, etc.). Several techniques have been proposed in order to alleviate this problem, and numerous power models have been adopted to predict the servers ...
Gribaudo, Marco, Pinciroli, Riccardo, Trivedi, Kishor   +2 more
openaire   +1 more source

Hardware Acceleration of Stochastic Neural Networks Enabled by Bit‐Cell Level Co‐Design of Magnetic Tunnel Junctions

Advanced Science, EarlyView.
An MTJ‐based probabilistic processing‐in‐memory architecture unites nonvolatile storage, local entropy generation, and stochastic computation within each memory cell. Stored multi‐bit weights are converted directly into programmable stochastic bitstreams, enabling neural‐network inference, adaptive Ising optimization, and uncertainty‐aware Bayesian ...
Qiuyuan Wang, Dooyong Koh, Brooke McGoldrick, Tudor Mocioi, Yabin Fan, Marc Baldo, Luqiao Liu   +6 more
wiley   +1 more source

Mixed uncertainty quantification of ship non-contact underwater explosion system

Zhongguo Jianchuan Yanjiu
ObjectivesSubstantial epistemic and aleatory uncertainties coexist in the modeling and simulation (M&S) of ship non-contact underwater explosion (UNDEX).
Xiao LIANG, Yiyi YANG, Jiangtao CHEN, Ruili WANG   +3 more
doaj   +1 more source

Mechanism‐Informed Language Modeling and Oxygenated 3D Screening Identify Berberine–Enzalutamide Synergy in Prostate Cancer Models

Advanced Science, EarlyView.
By integrating knowledge‐augmented artificial intelligence with an oxygen‐supplemented 3D screening platform, a synergistic interaction between berberine and enzalutamide is identified. The framework integrates compound mechanisms of action with disease‐relevant signaling context to prioritize candidate combinations and generate testable pathway‐based ...
Chih‐Hui Lo, Katie Shi, Lina Kafadarian, Alexandra Bermudez, Johnny Diaz, Liam Edwards, Yunqi Hong, Ziyi Chen, Hyeonji Hwang, Weihong Yan, Alan Levinson, Robert Damoiseaux, Cho‐Jui Hsieh, Tanya Stoyanova, Andrew S. Goldstein, Neil Y.C. Lin   +15 more
wiley   +1 more source

Informing synthetic passive microwave predictions through Bayesian deep learning with uncertainty decomposition

Environmental Data Science
Space-borne passive microwave (PMW) data provide rich information on atmospheric state, including cloud structure and underlying surface properties. However, PMW data are sparse and limited due to low Earth orbit collection, resulting in coarse Earth ...
Pedro Ortiz, Eleanor Casas, Marko Orescanin, Scott W. Powell, Veljko Petkovic   +4 more
doaj   +1 more source