Fetching the paper…
Reading the bibliography…
Recent work has observed that one can outperform exact inference in Bayesian neural networks by tuning the "temperature" of the posterior on a validation set (the "cold posterior" effect).
Probable networks and plausible predictions-a review of practical bayesian methods for supervised neural networks
MacKay, D. J · 1995
Earlier work this paper cites.
Priors for infinite networks
Neal, R. M · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Gaussian processes for machine learning , volume 2
Williams, C. K. and Rasmussen, C. E · 2006
Earlier work this paper cites.
Aleatory or epistemic? does it matter?
Der Kiureghian, A. and Ditlevsen, O · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Elliptical slice sampling
Murray, I., Adams, R., and MacKay, D · 2010
Cited alongside, same era.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G · 2013
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
How to start training: The effect of initialization and architecture
Hanin, B. and Rolnick, D · 2018
Cited alongside, same era.
Deep neural networks as gaussian processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J · 2018
Cited alongside, same era.
Gaussian process behaviour in wide deep neural networks
Matthews, A. G. d. G., Hron, J., Rowland, M., Turner, R. E., and Ghahramani, Z · 2018
Later among the works it cites.
Neural tangents: Fast and easy infinite neural networks in python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 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., Lakshminarayanan, B., and Snoek, J · 2019
Later among the works it cites.
How good is the bayes posterior in deep neural networks really?
Wenzel, F., Roth, K., Veeling, B. S., Świątkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., and Nowozin, S · 2020
Closest in time.
The case for bayesian deep learning
Wilson, A. G · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…