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Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation.
Design and analysis of computer experiments
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989) · 1989
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Query by committee
Seung, H. S., Opper, M., and Sompolinsky, H. (1992) · 1992
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Cytoscape: a software environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003) · 2003
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Gaussian processes in machine learning
Rasmussen, C. E. (2004) · 2004
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Engineering design via surrogate modelling: a practical guide
Forrester, A., Keane, A., et al. (2008) · 2008
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The million song dataset
Bertin-Mahieux, T., Ellis, D. P., Whitman, B., and Lamere, P. (2011) · 2011
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2d image registration in ct images using radial image descriptors
Graf, F., Kriegel, H.-P., Schubert, M., Pölsterl, S., and Cavallaro, A. (2011) · 2011
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Practical variational inference for neural networks
Graves, A. (2011) · 2011
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Variational bayesian inference with stochastic search
Paisley, J., Blei, D. M., and Jordan, M. I. (2012) · 2012
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Active learning
Settles, B. (2012) · 2012
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Gaussian processes for big data
Hensman, J., Fusi, N., and Lawrence, N. D. (2013) · 2013
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Detailed modeling of drilling fluid flow in a wellbore annulus while drilling
Podryabinkin, E., Rudyak, V., Gavrilov, A., and May, R. (2013) · 2013
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Feedback prediction for blogs
Buza, K. (2014) · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A. (2014) · 2014
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Adaptive design of experiments based on gaussian processes
Burnaev, E. and Panov, M. (2015) · 2015
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A proactive intelligent decision support system for predicting the popularity of online news
Fernandes, K., Vinagre, P., and Cortez, P. (2015) · 2015
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Cltune: A generic auto-tuner for opencl kernels
Nugteren, C. and Codreanu, V. (2015) · 2015
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Deep neural networks as gaussian processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J. (2017) · 2017
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Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., and Tkatchenko, A. (2017) · 2017
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The power of ensembles for active learning in image classification
Beluch, W. H., Genewein, T., Nürnberger, A., and Köhler, J. M. (2018) · 2018
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Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., and Eslami, S. (2018) · 2018
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Reliable uncertainty estimates in deep neural networks using noise contrastive priors
Hafner, D., Tran, D., Irpan, A., Lillicrap, T., and Davidson, J. (2018) · 2018
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
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Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
Mentch, L. and Hooker, G. (2016) · 2016
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Uci machine learning repository [http://archive. ics. uci. edu/ml]
Dua, D. and Taniskidou, E. K. (2017) · 2017
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Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z. (2017) · 2017
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Research and development of neural network ensembles: a survey
Li, H., Wang, X., and Ding, S. (2018) · 2018
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Gaussian process behaviour in wide deep neural networks
Matthews, A. G. d. G., Rowland, M., Hron, J., Turner, R. E., and Ghahramani, Z. (2018) · 2018
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Pop, R. and Fulop, P. (2018) · 2018
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Differentiable compositional kernel learning for gaussian processes
Sun, S., Zhang, G., Wang, C., Zeng, W., Li, J., and Grosse, R. (2018) · 2018
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Dropout-based active learning for regression
Tsymbalov, E., Panov, M., and Shapeev, A. (2018) · 2018
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