Dropout as a Bayesian Approximation: Appendix

On the likelihood that one unknown probability exceeds another in view of the evidence of two samples

Thompson, W. R. (1933) · 1933

Earlier work this paper cites.

Dropout: A simple way to prevent neural networks from overfitting

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958

Earlier work this paper cites.

Doubly stochastic variational Bayes for non-conjugate inference

Titsias, M. and Lázaro-Gredilla, M. (2014) · 1979

Earlier work this paper cites.

Long short-term memory

Hochreiter, S. and Schmidhuber, J. (1997) · 1997

Earlier work this paper cites.

Computing with infinite networks

Williams, C. K. (1997) · 1997

Earlier work this paper cites.

Marginalized kernels for biological sequences

Tsuda, K., Kin, T., and Asai, K. (2002) · 2002

Earlier work this paper cites.

Gaussian process dynamical models

Wang, J., Hertzmann, A., and Blei, D. M. (2005) · 2005

Earlier work this paper cites.

Neural probabilistic language models

Bengio, Y., Schwenk, H., Senécal, J.-S., Morin, F., and Gauvain, J.-L. (2006) · 2006

Earlier work this paper cites.

Pattern Recognition and Machine Learning (Information Science and Statistics)

Bishop, C. M. (2006) · 2006

Earlier work this paper cites.

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Rasmussen, C. E. and Williams, C. K. I. (2006) · 2006

Earlier work this paper cites.

Sparse spectrum Gaussian process regression

Lázaro-Gredilla, M., Quiñonero-Candela, J., Rasmussen, C. E., and Figueiras-Vidal, A. R. (2010) · 2010

Cited alongside, same era.

Algorithms for reinforcement learning

Szepesvári, C. (2010) · 2010

Cited alongside, same era.

Bayesian Gaussian process latent variable model

Titsias, M. and Lawrence, N. (2010) · 2010

Cited alongside, same era.

Variational Bayesian inference with stochastic search

Blei, D. M., Jordan, M. I., and Paisley, J. W. (2012) · 2012

Cited alongside, same era.

Improving neural networks by preventing co-adaptation of feature detectors

Original

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R. (2012) · 2012

Cited alongside, same era.

Imagenet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012

Stochastic variational inference

Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013

Later among the works it cites.

Auto-encoding variational Bayes

Original

Kingma, D. P. and Welling, M. (2013) · 2013

Later among the works it cites.

Playing atari with deep reinforcement learning

Original

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013

Later among the works it cites.

Dropout training as adaptive regularization

Wager, S., Wang, S., and Liang, P. S. (2013) · 2013

Later among the works it cites.

Distributed variational inference in sparse Gaussian process regression and latent variable models

Gal, Y., van der Wilk, M., and Rasmussen, C. (2014) · 2014

Later among the works it cites.