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Eric Nalisnick
Eric Nalisnick
Assistant Professor, Johns Hopkins University
Verified email at jhu.edu - Homepage
Title
Cited by
Cited by
Year
Normalizing flows for probabilistic modeling and inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
Journal of Machine Learning Research 22 (57), 1-64, 2021
14122021
Do deep generative models know what they don't know?
E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan
arXiv preprint arXiv:1810.09136, 2018
7332018
Improving document ranking with dual word embeddings
E Nalisnick, B Mitra, N Craswell, R Caruana
Proceedings of the 25th international conference companion on world wide web …, 2016
1992016
Stick-breaking variational autoencoders
E Nalisnick, P Smyth
arXiv preprint arXiv:1605.06197, 2016
1982016
A dual embedding space model for document ranking
B Mitra, E Nalisnick, N Craswell, R Caruana
arXiv preprint arXiv:1602.01137, 2016
1772016
Detecting out-of-distribution inputs to deep generative models using typicality
E Nalisnick, A Matsukawa, YW Teh, B Lakshminarayanan
arXiv preprint arXiv:1906.02994, 2019
1722019
Bayesian batch active learning as sparse subset approximation
R Pinsler, J Gordon, E Nalisnick, JM Hernández-Lobato
Advances in neural information processing systems 32, 2019
1332019
Character-to-character sentiment analysis in Shakespeare’s plays
ET Nalisnick, HS Baird
Proceedings of the 51st Annual Meeting of the Association for Computational …, 2013
1062013
Approximate inference for deep latent gaussian mixtures
E Nalisnick, L Hertel, P Smyth
NIPS Workshop on Bayesian Deep Learning 2, 131, 2016
1052016
Hybrid models with deep and invertible features
E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan
International Conference on Machine Learning, 4723-4732, 2019
862019
Bayesian deep learning via subnetwork inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
International Conference on Machine Learning, 2510-2521, 2021
85*2021
Extracting sentiment networks from Shakespeare's plays
ET Nalisnick, HS Baird
2013 12th International Conference on Document Analysis and Recognition, 758-762, 2013
442013
Dropout as a structured shrinkage prior
E Nalisnick, JM Hernández-Lobato, P Smyth
International Conference on Machine Learning, 4712-4722, 2019
432019
On priors for Bayesian neural networks
ET Nalisnick
University of California, Irvine, 2018
342018
Calibrated learning to defer with one-vs-all classifiers
R Verma, E Nalisnick
International Conference on Machine Learning, 22184-22202, 2022
272022
A scale mixture perspective of multiplicative noise in neural networks
E Nalisnick, A Anandkumar, P Smyth
arXiv preprint arXiv:1506.03208, 2015
242015
Adapting the linearised laplace model evidence for modern deep learning
J Antorán, D Janz, JU Allingham, E Daxberger, RR Barbano, E Nalisnick, ...
International Conference on Machine Learning, 796-821, 2022
232022
Predictive complexity priors
E Nalisnick, J Gordon, JM Hernández-Lobato
International Conference on Artificial Intelligence and Statistics, 694-702, 2021
232021
Learning priors for invariance
E Nalisnick, P Smyth
International Conference on Artificial Intelligence and Statistics, 366-375, 2018
202018
Do Bayesian Neural Networks Need To Be Fully Stochastic?
M Sharma, S Farquhar, E Nalisnick, T Rainforth
International Conference on Artificial Intelligence and Statistics, 7694-7722, 2023
192023
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