Fetching the paper…
Reading the bibliography…
Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data.
Conformal prediction under covariate shift
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J · 1904
Earlier work this paper cites.
Predictive inference with the jackknife+
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J · 1905
Earlier work this paper cites.
Notes on bias in estimation
Quenouille, M. H · 1956
Earlier work this paper cites.
Bias and confidence in not quite large samples
Tukey, J · 1958
Earlier work this paper cites.
The jackknife-a review
Miller, R. G · 1974
Earlier work this paper cites.
The jackknife and the bootstrap for general stationary observations
Kunsch, H. R · 1989
Earlier work this paper cites.
Jackknife-after-bootstrap standard errors and influence functions
Efron, B · 1992
Earlier work this paper cites.
Fast exact multiplication by the hessian
Pearlmutter, B. A · 1994
Earlier work this paper cites.
A quantile regression neural network approach to estimating the conditional density of multiperiod returns
Taylor, J. W · 2000
Earlier work this paper cites.
Quantile regression
Koenker, R. and Hallock, K. F · 2001
Earlier work this paper cites.
Reinforcement learning with long short-term memory
Bakker, B · 2002
Earlier work this paper cites.
White cell count and intensive care unit outcome
Waheed, U., Williams, P., Brett, S., Baldock, G., and Soni, N · 2003
Earlier work this paper cites.
The interplay of bayesian and frequentist analysis
Bayarri, M. J. and Berger, J. O · 2004
Earlier work this paper cites.
Frequentist prediction intervals and predictive distributions
Lawless, J. and Fredette, M · 2005
Earlier work this paper cites.
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Graves, A., Fernández, S., Gomez, F., and Schmidhuber, J · 2006
Earlier work this paper cites.
Expression of uncertainty in linguistic data
Auger, A. and Roy, J · 2008
Earlier work this paper cites.
Recursive bayesian recurrent neural networks for time-series modeling
Mirikitani, D. T. and Nikolaev, N · 2009
Earlier work this paper cites.
Robust statistics: the approach based on influence functions , volume 196
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., and Stahel, W. A · 2011
Earlier work this paper cites.
Estimating beta-mixing coefficients
Mcdonald, D., Shalizi, C., and Schervish, M · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
Earlier work this paper cites.
Coverage properties of confidence intervals for generalized additive model components
Marra, G. and Wood, S. N · 2012
Cited alongside, same era.
Hematologic impact of antibiotic administration on patients taking clozapine
Shuman, M., Demler, T. L., Trigoboff, E., and Opler, L. A · 2012
Cited alongside, same era.
Lstm neural networks for language modeling
Sundermeyer, M., Schlüter, R., and Ney, H · 2012
Cited alongside, same era.
Theory of optimal experiments
Fedorov, V. V · 2013
Cited alongside, same era.
Recurrent continuous translation models
Kalchbrenner, N. and Blunsom, P · 2013
Cited alongside, same era.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q · 2014
Cited alongside, same era.
Structured inference networks for nonlinear state space models
Krishnan, R. G., Shalit, U., and Sontag, D · 2017
Later among the works it cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Later among the works it cites.
Stock price prediction using lstm, rnn and cnn-sliding window model
Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V. K., and Soman, K · 2017
Later among the works it cites.
Deep ehr: a survey of recent advances in deep learning techniques for electronic health record (ehr) analysis
Shickel, B., Tighe, P. J., Bihorac, A., and Rashidi, P · 2017
Later among the works it cites.
A multi-horizon quantile recurrent forecaster
Wen, R., Torkkola, K., Narayanaswamy, B., and Madeka, D · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Wager, S., Hastie, T., and Efron, B · 2014
Cited alongside, same era.
Bayesian recurrent neural network for language modeling
Chien, J.-T. and Ku, Y.-C · 2015
Cited alongside, same era.
Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
Cited alongside, same era.
Deep and confident prediction for time series at uber
Zhu, L. and Laptev, N · 2015
Cited alongside, same era.
Second-order stochastic optimization in linear time
Agarwal, N., Bullins, B., and Hazan, E · 2016
Cited alongside, same era.
Uncertainty in deep learning
Gal, Y · 2016
Cited alongside, same era.
A hidden absorbing semi-markov model for informatively censored temporal data: Learning and inference
Alaa, A. M. and Van Der Schaar, M · 2018
Later among the works it cites.
Countdown regression: sharp and calibrated survival predictions
Avati, A., Duan, T., Jung, K., Shah, N. H., and Ng, A · 2018
Later among the works it cites.
The economics of risk and uncertainty
Gollier, C · 2018
Later among the works it cites.
Forecasting treatment responses over time using recurrent marginal structural networks
Lim, B., Alaa, A., and Schaar, M. v. d · 2018
Later among the works it cites.
Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
Later among the works it cites.
Deep state space models for time series forecasting
Rangapuram, S. S., Seeger, M. W., Gasthaus, J., Stella, L., Wang, Y., and Januschowski, T · 2018
Later among the works it cites.
A scalable laplace approximation for neural networks
Ritter, H., Botev, A., and Barber, D · 2018
Later among the works it cites.
Estimation and inference of heterogeneous treatment effects using random forests
Wager, S. and Athey, S · 2018
Later among the works it cites.
Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation
Wang, L., Zhang, W., He, X., and Zha, H · 2018
Later among the works it cites.
Attentive state-space modeling of disease progression
Alaa, A. M. and van der Schaar, M · 2019
Later among the works it cites.
Analyzing the role of model uncertainty for electronic health records
Dusenberry, M. W., Tran, D., Choi, E., Kemp, J., Nixon, J., Jerfel, G., Heller, K., and Dai, A. M · 2019
Later among the works it cites.
Probabilistic forecasting with spline quantile function rnns
Gasthaus, J., Benidis, K., Wang, Y., Rangapuram, S. S., Salinas, D., Flunkert, V., and Januschowski, T · 2019
Later among the works it cites.
On the accuracy of influence functions for measuring group effects
Koh, P. W., Ang, K.-S., Teo, H. H., and Liang, P · 2019
Later among the works it cites.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., and Wilson, A. G · 2019
Later among the works it cites.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 2019
Later among the works it cites.