Results 71 to 80 of about 471,788 (278)

Improved learning of Bayesian networks

CoRR, 2001
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayesian Networks is ordered by DAG Markov model inclusion and it is natural to consider that a good search policy should take this into account.
Kocka, T., Castelo Valdueza, R.
openaire   +4 more sources

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

STEM-Based Bayesian Computational Learning Model-BCLM for Effective Learning of Bayesian Statistics

IEEE Access
This work contributes to the comprehension of Bayes’ theorem inclusive Bayesian probabilities and Bayesian inferencing within the framework of STEM (Science, Technology, Engineering, Arts, and Mathematics) and cognitive learning w.r.t Bloom’
Ikram E. Khuda, Sadique Ahmad, Abdelhamied Ashraf Ateya   +2 more
doaj   +1 more source

Bayesian Policy Gradients via Alpha Divergence Dropout Inference

, 2011
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult.
Henderson, Peter, Doan, Thang, Islam, Riashat, Meger, David   +3 more
core   +3 more sources

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Differential effects of reward and punishment in decision making under uncertainty: a computational study.

Frontiers in Neuroscience, 2014
Computational models of learning have proved largely successful in characterising potentialmechanisms which allow humans to make decisions in uncertain and volatile contexts.
Elaine eDuffin, Amy Rachel Bland, Alexandre eSchaefer, Marc eDe Kamps   +3 more
doaj   +1 more source

A Tutorial on Learning with Bayesian Networks [PDF]

, 1998
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are ...
openaire   +2 more sources

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Entropy bounds on Bayesian learning [PDF]

Journal of Mathematical Economics, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gossner, Olivier, Tomala, Tristan
openaire   +6 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source