Results 261 to 270 of about 9,437,008 (290)

Machine learning-assisted development of a fast Mechanochemical Johnson-Corey-Chaykovsky reaction. [PDF]

Nat Commun
Mele F, Constantin AM, Barezzi M, Rossi L, Ergasti A, Fontanini T, Civardi S, Sundermann RM, Mazzeo PP, Maggi R, Della Ca' N, Prati A, Capaldo L.   +12 more
europepmc   +1 more source

From empirical imprinting to programmable synthetic receptors: a perspective on data science and machine learning for molecularly imprinted polymers.

Polym Chem
Alsholi A, Tiwari S, Zhang J, Peeters M.
europepmc   +1 more source

Pockets to products: a data-driven approach for classification of monoterpene synthases.

Digit Discov
Ó Raghallaigh C, Scrutton NS, Hay S.
europepmc   +1 more source

Exploring Active Learning in a Bayesian Framework

, 2010
Denton, Stephen, Kruschke, John
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Bayesian deep-learning for RUL prediction: An active learning perspective

Reliability Engineering and System Safety, 2022
Zhisheng Ye, Weiwen Peng
exaly   +3 more sources

Bayesian Active Learning for Drug Combinations

IEEE Transactions on Biomedical Engineering, 2013
The dynamics of complex diseases are governed by intricate interactions of myriad factors. Drug combinations, formed by mixing several single-drug treatments at various doses, can enhance the effectiveness of the therapy by targeting multiple contributing factors.
Mijung Park, Marcel Nassar, Haris Vikalo
openaire   +3 more sources

Bayesian Active Learning With Non-Persistent Noise

IEEE Transactions on Information Theory, 2015
We consider the problem of noisy Bayesian active learning where we are given a finite set of functions $\mathcal {H}$ , a sample space $\mathcal {X}$ , and a label set $\mathcal {L}$ . One of the functions in $\mathcal {H}$ assigns labels to samples in $\mathcal {X}$ .
Mohammad Naghshvar, Tara Javidi, Kamalika Chaudhuri   +2 more
openaire   +1 more source

Active learning for sparse bayesian multilabel classification

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational ...
Deepak Vasisht, Andreas C. Damianou, Manik Varma, Ashish Kapoor   +3 more
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Active learning for online bayesian matrix factorization

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012
The problem of large-scale online matrix completion is addressed via a Bayesian approach. The proposed method learns a factor analysis (FA) model for large matrices, based on a small number of observed matrix elements, and leverages the statistical model to actively select which new matrix entries/observations would be most informative if they could be
Jorge G. Silva, Lawrence Carin
openaire   +2 more sources