Eric Nalisnick
Eric Nalisnick
Assistant Professor, University of Amsterdam
Verified email at uci.edu - Homepage
Title
Cited by
Cited by
Year
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
2222018
Normalizing flows for probabilistic modeling and inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
arXiv preprint arXiv:1912.02762, 2019
1862019
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
1612016
A dual embedding space model for document ranking
B Mitra, E Nalisnick, N Craswell, R Caruana
arXiv preprint arXiv:1602.01137, 2016
1212016
Stick-breaking variational autoencoders
E Nalisnick, P Smyth
arXiv preprint arXiv:1605.06197, 2016
116*2016
Approximate inference for deep latent gaussian mixtures
E Nalisnick, L Hertel, P Smyth
NIPS Workshop on Bayesian Deep Learning 2, 131, 2016
622016
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
512013
Detecting out-of-distribution inputs to deep generative models using a test for typicality
E Nalisnick, A Matsukawa, YW Teh, B Lakshminarayanan
arXiv preprint arXiv:1906.02994 5, 5, 2019
492019
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
382019
Extracting sentiment networks from Shakespeare's plays
ET Nalisnick, HS Baird
2013 12th International Conference on Document Analysis and Recognition, 758-762, 2013
362013
Bayesian batch active learning as sparse subset approximation
R Pinsler, J Gordon, E Nalisnick, JM Hernández-Lobato
arXiv preprint arXiv:1908.02144, 2019
282019
A scale mixture perspective of multiplicative noise in neural networks
E Nalisnick, A Anandkumar, P Smyth
arXiv preprint arXiv:1506.03208, 2015
17*2015
Dropout as a structured shrinkage prior
E Nalisnick, JM Hernández-Lobato, P Smyth
International Conference on Machine Learning, 4712-4722, 2019
162019
Infinite dimensional word embeddings
E Nalisnick, S Ravi
15*2017
On priors for bayesian neural networks
ET Nalisnick
UC Irvine, 2018
102018
Learning priors for invariance
E Nalisnick, P Smyth
International Conference on Artificial Intelligence and Statistics, 366-375, 2018
82018
Analyzing NIH Funding Patterns over Time with Statistical Text Analysis.
J Park, M Blume-Kohout, R Krestel, ET Nalisnick, P Smyth
AAAI Workshop: Scholarly Big Data, 2016
62016
Learning approximately objective priors
E Nalisnick, P Smyth
arXiv preprint arXiv:1704.01168, 2017
52017
The amortized bootstrap
E Nalisnick, P Smyth
ICML Workshop on Implicit Models, 2017
32017
Bayesian trees for automated cytometry data analysis
D Ji, E Nalisnick, Y Qian, RH Scheuermann, P Smyth
Machine Learning for Healthcare Conference, 465-483, 2018
22018
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