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Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability.
Quantifying intrinsic uncertainty in classification via deep dirichlet mixture networks
Wu, Q., Li, H., Li, L., and Yu, Z · 1906
Earlier work this paper cites.
The well-calibrated Bayesian
Dawid, A. P · 1982
Earlier work this paper cites.
Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition
Bridle, J. S · 1990
Earlier work this paper cites.
A practical Bayesian framework for backpropagation networks
MacKay, D. J · 1992
Earlier work this paper cites.
Probable networks and plausible predictions—a review of practical Bayesian methods for supervised neural networks
MacKay, D. J · 1995
Earlier work this paper cites.
Natural gradient works efficiently in learning
Amari, S.-I · 1998
Earlier work this paper cites.
Efficient backprop
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K.-R · 1998
Earlier work this paper cites.
Accelerated gradient descent by factor-centering decomposition
Schraudolph, N · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1999
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Belkin, M., Niyogi, P., and Sindhwani, V · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Graves, A · 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.
Deep learning made easier by linear transformations in perceptrons
Raiko, T., Valpola, H., and LeCun, Y · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
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
Earlier work this paper cites.
Mean-normalized stochastic gradient for large-scale deep learning
Wiesler, S., Richard, A., Schlüter, R., and Ney, H · 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.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
A complete recipe for stochastic gradient MCMC
Ma, Y.-A., Chen, T., and Fox, E · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using Bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M · 2015
Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M · 2017
Later among the works it cites.
A variational Dirichlet framework for out-of-distribution detection
Chen, W., Shen, Y., Jin, H., and Wang, W · 2018
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Later among the works it cites.
Noise contrastive priors for functional uncertainty
Hafner, D., Tran, D., Lillicrap, T., Irpan, A., and Davidson, J · 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.
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Cited alongside, same era.
Uncertainty in Deep Learning
Gal, Y · 2016
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Disturblabel: Regularizing cnn on the loss layer
Xie, L., Wang, J., Wei, Z., Wang, M., and Tian, Q · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
Later among the works it cites.
Relaxed softmax: Efficient confidence auto-calibration for safe pedestrian detection
Neumann, L., Zisserman, A., and Vedaldi, A · 2018
Later among the works it cites.
Realistic evaluation of deep semi-supervised learning algorithms
Oliver, A., Odena, A., Raffel, C. A., Cubuk, E. D., and Goodfellow, I · 2018
Later among the works it cites.
Evidential deep learning to quantify classification uncertainty
Sensoy, M., Kaplan, L., and Kandemir, M · 2018
Later among the works it cites.
Noisy natural gradient as variational inference
Zhang, G., Sun, S., Duvenaud, D., and Grosse, R · 2018
Later among the works it cites.
Meta-learning for stochastic gradient MCMC
Gong, W., Li, Y., and Hernández-Lobato, J. M · 2019
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2019
Later among the works it cites.
Bayesian inference for large scale image classification
Heek, J. and Kalchbrenner, N · 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.
Reverse KL-divergence training of prior networks: Improved uncertainty and adversarial robustness
Malinin, A. and Gales, M · 2019
Later among the works it cites.
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Müller, R., Kornblith, S., and Hinton, G · 2019
Later among the works it cites.
Practical deep learning with Bayesian principles
Osawa, K., Swaroop, S., Jain, A., Eschenhagen, R., Turner, R. E., Yokota, R., and Khan, M. E · 2019
Later among the works it cites.
Functional variational Bayesian neural networks
Sun, S., Zhang, G., Shi, J., and Grosse, R · 2019
Later among the works it cites.