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Eric Nalisnick
Eric Nalisnick
Assistant Professor, Johns Hopkins University
Preverjeni e-poštni naslov na jhu.edu - Domača stran
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Leto
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
18512021
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
8382018
Stick-breaking variational autoencoders
E Nalisnick, P Smyth
arXiv preprint arXiv:1605.06197, 2016
2062016
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
2062016
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
2042019
A dual embedding space model for document ranking
B Mitra, E Nalisnick, N Craswell, R Caruana
arXiv preprint arXiv:1602.01137, 2016
1842016
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
1472019
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
1212013
Bayesian deep learning via subnetwork inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
International Conference on Machine Learning, 2510-2521, 2021
119*2021
Approximate inference for deep latent gaussian mixtures
E Nalisnick, L Hertel, P Smyth
NIPS Workshop on Bayesian Deep Learning 2, 131, 2016
1122016
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
982019
Dropout as a structured shrinkage prior
E Nalisnick, JM Hernández-Lobato, P Smyth
International Conference on Machine Learning, 4712-4722, 2019
492019
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
472023
Extracting sentiment networks from Shakespeare's plays
ET Nalisnick, HS Baird
2013 12th International Conference on Document Analysis and Recognition, 758-762, 2013
472013
Calibrated learning to defer with one-vs-all classifiers
R Verma, E Nalisnick
International Conference on Machine Learning, 22184-22202, 2022
442022
On priors for Bayesian neural networks
ET Nalisnick
University of California, Irvine, 2018
392018
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
322022
Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles
R Verma, D Barrejón, E Nalisnick
International Conference on Artificial Intelligence and Statistics, 11415-11434, 2023
312023
A scale mixture perspective of multiplicative noise in neural networks
E Nalisnick, A Anandkumar, P Smyth
arXiv preprint arXiv:1506.03208, 2015
272015
Predictive complexity priors
E Nalisnick, J Gordon, JM Hernández-Lobato
International Conference on Artificial Intelligence and Statistics, 694-702, 2021
252021
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