Fetching the paper…
Reading the bibliography…
We introduce a new deep generative model useful for uncertainty quantification: the Morse neural network, which generalizes the unnormalized Gaussian densities to have modes of high-dimensional submanifolds instead of just discrete points.
The behavior of a function on its critical set
Morse, A. P · 1939
Earlier work this paper cites.
The measure of the critical values of differentiable maps
Sard, A · 1942
Earlier work this paper cites.
Morse-Bott theory and equivariant cohomology
Austin, D. M. and Braam, P. J · 1995
Earlier work this paper cites.
Differential topology
Hirsch, M. W · 1997
Earlier work this paper cites.
A proof of the Morse-Bott lemma
Banyaga, A. and Hurtubise, D. E · 2004
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A. M., Yosinski, J., and Clune, J · 2015
Earlier work this paper cites.
Information Geometry and Its Applications
Amari, S.-i · 2016
Earlier work this paper cites.
Concrete problems in AI safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P. F., Schulman, J., and Mané, D · 2016
Earlier work this paper cites.
Deep Learning
Goodfellow, I. J., Bengio, Y., and Courville, A · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
Earlier work this paper cites.
Deep one-class classification
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., and Kloft, M · 2018
Earlier work this paper cites.
An evaluation dataset for intent classification and out-of-scope prediction
Larson, S., Mahendran, A., Peper, J. J., Clarke, C., Lee, A., Hill, P., Kummerfeld, J. K., Leach, K., Laurenzano, M. A., Tang, L., and Mars, J · 2019
Cited alongside, same era.
Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Likelihood ratios for out-of-distribution detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., Depristo, M., Dillon, J., and Lakshminarayanan, B · 2019
Cited alongside, same era.
Anomalous example detection in deep learning: A survey
Bulusu, S., Kailkhura, B., Li, B., Varshney, P. K., and Song, D. X · 2020
Cited alongside, same era.
Density of states estimation for out of distribution detection
Morningstar, W. R., Ham, C., Gallagher, A. G., Lakshminarayanan, B., Alemi, A., and Dillon, J. V · 2021
Later among the works it cites.
Understanding the failure modes of out-of-distribution generalization
Nagarajan, V., Andreassen, A., and Neyshabur, B · 2021
Later among the works it cites.
A simple fix to Mahalanobis distance for improving near-ood detection
Ren, J., Fort, S., Liu, J. Z., Roy, A. G., Padhy, S., and Lakshminarayanan, B · 2021
Later among the works it cites.
Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions
Roy, A. G., Ren, J. J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z. W., Vo, N., Bui, P., Winter, S., MacWilliams, P., Corrado, G., Telang, U., Liu, Y., Cemgil, T., Karthikesalingam, A., Lakshminarayanan, B., and Winkens, J · 2021
Later among the works it cites.
ILCOC: An incremental learning framework based on contrastive one-class classifiers
Sun, W., Zhang, J., Wang, D., Geng, Y.-a., and Li, Q · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
Che, T., Zhang, R., Sohl-Dickstein, J., Larochelle, H., Paull, L., Cao, Y., and Bengio, Y · 2020
Cited alongside, same era.
Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Liu, J. Z., Lin, Z., Padhy, S., Tran, D., Bedrax-Weiss, T., and Lakshminarayanan, B · 2020
Cited alongside, same era.
Generalized energy based models
Arbel, M., Zhou, L., and Gretton, A · 2021
Cited alongside, same era.
A connection between cut locus, Thom space and Morse-Bott functions
Basu, S. and Prasad, S · 2021
Cited alongside, same era.
Learning energy-based models by diffusion recovery likelihood
Gao, R., Song, Y., Poole, B., Wu, Y. N., and Kingma, D. P · 2021
Cited alongside, same era.
Reliable graph neural networks for drug discovery under distributional shift
Han, K., Lakshminarayanan, B., and Liu, J. Z · 2021
Cited alongside, same era.
Continual learning by using information of each class holistically
Hu, W., Qin, Q., Wang, M., Ma, J., and Liu, B · 2021
Cited alongside, same era.
Later among the works it cites.
Energy-based open-world uncertainty modeling for confidence calibration
Wang, Y., Li, B., Che, T., Zhou, K., Liu, Z., and Li, D · 2021
Later among the works it cites.
Conjugate energy-based models
Wu, H., Esmaeili, B., Wick, M. L., Tristan, J.-B., and van de Meent, J.-W · 2021
Later among the works it cites.
A simple approach to improve single-model deep uncertainty via distance-awareness
Liu, J. Z., Padhy, S., Ren, J., Lin, Z., Wen, Y., Jerfel, G., Nado, Z., Snoek, J., Tran, D., and Lakshminarayanan, B · 2022
Later among the works it cites.
Using mixup as a regularizer can surprisingly improve accuracy & out-of-distribution robustness
Pinto, F., Yang, H., Lim, S.-N., Torr, P., and Dokania, P. K · 2022
Later among the works it cites.
A unified survey on anomaly, novelty, open-set, and out of-distribution detection: Solutions and future challenges
Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., and Sabokrou, M · 2022
Later among the works it cites.
Deep learning-based object detection and scene perception under bad weather conditions
Sharma, T., Debaque, B., Duclos, N., Chehri, A., Kinder, B., and Fortier, P · 2022
Later among the works it cites.
Poisson flow generative models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T. S · 2022
Later among the works it cites.
Energy-based generative adversarial networks
Zhao, J., Mathieu, M., and LeCun, Y · 2022
Later among the works it cites.
Probabilistic Machine Learning: Advanced Topics
Murphy, K. P · 2023
Closest in time.
Cut locus of submanifolds: A geometric and topological viewpoint
Prasad, S · 2023
Closest in time.