Fetching the paper…
Reading the bibliography…
We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form.
Singular value decomposition and least squares solutions
Golub, G. H. and Reinsch, C · 1971
Earlier work this paper cites.
Principal component analysis
Wold, S., Esbensen, K., and Geladi, P · 1987
Earlier work this paper cites.
Computing a nearest symmetric positive semidefinite matrix
Higham, N. J · 1988
Earlier work this paper cites.
Improving the convergence of back-propagation learning with second-order methods
Becker, S. and Lecun, Y · 1989
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E. and van Camp, D · 1993
Earlier work this paper cites.
Bayesian Learning for Neural 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.
Thin junction tree filters for simultaneous localization and mapping
Paskin, M. A · 2003
Earlier work this paper cites.
Simultaneous localization and mapping with sparse extended information filters
Thrun, S., Liu, Y., Koller, D., Ng, A. Y., Ghahramani, Z., and Durrant-Whyte, H · 2004
Earlier work this paper cites.
Multi-robot slam with sparse extended information filers
Thrun, S. and Liu, Y · 2005
Earlier work this paper cites.
Simultaneous localization and mapping (slam): Part ii
Bailey, T. and Durrant-Whyte, H · 2006
Earlier work this paper cites.
Exactly sparse delayed-state filters for view-based slam
Eustice, R. M., Singh, H., and Leonard, J. J · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Dimensionality reduction: a comparative
Van Der Maaten, L., Postma, E., and Van den Herik, J · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Graves, A · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Knowing when we don’t know: Introspective classification for mission-critical decision making
Grimmett, H., Paul, R., Triebel, R., and Posner, I · 2013
Earlier work this paper cites.
Fast symmetric factorization of hierarchical matrices with applications
Ambikasaran, S. and O’Neil, M · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Introspective classification for robot perception
Grimmett, H., Triebel, R., Paul, R., and Posner, I · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of bayesian neural networks
Herandez-Lobato, J. M. and Adams, R. P · 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.
Variational Dropout and the Local Reparameterization Trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
Cited alongside, same era.
Optimizing neural networks with kronecker-factored approximate curvature
Martens, J. and Grosse, R. B · 2015
Cited alongside, same era.
Active online confidence boosting for efficient object classification
Mund, D., Triebel, R., and Cremers, D · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X · 2016
SLANG: fast structured covariance approximations for bayesian deep learning with natural gradient
Mishkin, A., Kunstner, F., Nielsen, D., Schmidt, M. W., and Khan, M. E · 2018
Later among the works it cites.
On the importance of strong baselines in bayesian deep learning
Mukhoti, J., Stenetorp, P., and Gal, Y · 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.
Online structured laplace approximations for overcoming catastrophic forgetting
Ritter, H., Botev, A., and Barber, D · 2018
Later among the works it cites.
Empirical analysis of the hessian of over-parametrized neural networks
Sagun, L., Evci, U., Güney, V. U., Dauphin, Y., and Bottou, L · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Uncertainty in Deep Learning
Gal, Y · 2016
Cited alongside, same era.
Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 2016
Cited alongside, same era.
Distributed second-order optimization using kronecker-factored approximations
Ba, J., Grosse, R. B., and Martens, J · 2017
Cited alongside, same era.
Practical Gauss-Newton optimisation for deep learning
Botev, A., Ritter, H., and Barber, D · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Zhang, G., Sun, S., Duvenaud, D. K., and Grosse, R. B · 2018
Later among the works it cites.
Introspective robot perception using smoothed predictions from bayesian neural networks
Feng, J., Durner, M., Marton, Z.-C., Balint-Benczedi, F., and Triebel, R · 2019
Later among the works it cites.
’in-between’uncertainty in bayesian neural networks
Foong, A. Y., Li, Y., Hernández-Lobato, J. M., and Turner, R. E · 2019
Later among the works it cites.
Subspace inference for bayesian deep learning
Izmailov, P., Maddox, W., Kirichenko, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
Later among the works it cites.
Limitations of the empirical fisher approximation for natural gradient descent
Kunstner, F., Hennig, P., and Balles, L · 2019
Later among the works it cites.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
Later among the works it cites.
Variational Laplace autoencoders
Park, Y., Kim, C., and Kim, G · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Composable effects for flexible and accelerated probabilistic programming in numpyro
Phan, D., Pradhan, N., and Jankowiak, M · 2019
Later among the works it cites.
Deterministic variational inference for robust bayesian neural networks
Wu, A., Nowozin, S., Meeds, E., Turner, R. E., Hernandez-Lobato, J. M., and Gaunt, A. L · 2019
Later among the works it cites.
Backpack: Packing more into backprop
Dangel, F., Kunstner, F., and Hennig, P · 2020
Closest in time.
Being bayesian, even just a bit, fixes overconfidence in relu networks
Kristiadi, A., Hein, M., and Hennig, P · 2020
Closest in time.
Visual-inertial telepresence for aerial manipulation
Lee, J., Balachandran, R., Sarkisov, Y. S., De Stefano, M., Coelho, A., Shinde, K., Kim, M. J., Triebel, R., and Kondak, K · 2020
Closest in time.
Non-parametric calibration for classification
Wenger, J., Kjellström, H., and Triebel, R · 2020
Closest in time.
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.