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Generating high quality uncertainty estimates for sequential regression, particularly deep recurrent networks, remains a challenging and open problem.
Evaluating scalable bayesian deep learning methods for robust computer vision
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Why does the GARCH(1,1) model fail to provide sensible longer- horizon volatility forecasts?
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Adam: A method for stochastic optimization, 2014
Kingma, D. P. and Ba, J · 2015
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A theoretically grounded application of dropout in recurrent neural networks
Gal, Y. and Ghahramani, Z · 2016
State-of-the-art speech recognition with sequence-to-sequence models
Chiu, C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., Jaitly, N., Li, B., Chorowski, J., and Bacchiani, M · 2018
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To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M., and Gupta, M · 2018
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Factsheets: Increasing trust in ai services through supplier’s declarations of conformity
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., Reimer, D., Richards, J., Tsay, J., and Varshney, K. R · 2019
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The need for uncertainty quantification in machine-assisted medical decision making
Begoli, E., Bhattacharya, T., and Kusnezov, D · 2019
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Optimal multi-step-ahead prediction of arch/garch models and novas transformation
Chen, J. and Politis, D. N · 2019
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Self-critical sequence training for image captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., and Goel, V · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Char2wav: End-to-end speech synthesis
Sotelo, J., Mehri, S., Kumar, K., Santos, J. F., Kastner, K., Courville, A. C., and Bengio, Y · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C
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Confidence scoring using whitebox meta-models with linear classifier probes
Chen, T., Navrátil, J., Iyengar, V., and Shanmugam, K · 2019
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Online meta-learning
Finn, C., Rajeswaran, A., Kakade, S., and Levine, S · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., and Nado, Z · 2019
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Advancing sequence-to-sequence based speech recognition
Tüske, Z., Audhkhasi, K., and Saon, G · 2019
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Crowd counting with decomposed uncertainty
Oh, M., Olsen, P. A., and Ramamurthy, K. N · 2020
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Wind power forecasting using multi-objective evolutionary algorithms for wavelet neural network-optimized prediction intervals
Shen, Y., Wang, X., and Chen, J · 2076
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