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
1422018
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
1412016
Stick-breaking variational autoencoders
E Nalisnick, P Smyth
arXiv preprint arXiv:1605.06197, 2016
97*2016
A dual embedding space model for document ranking
B Mitra, E Nalisnick, N Craswell, R Caruana
arXiv preprint arXiv:1602.01137, 2016
972016
Normalizing flows for probabilistic modeling and inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
arXiv preprint arXiv:1912.02762, 2019
872019
Approximate inference for deep latent gaussian mixtures
E Nalisnick, L Hertel, P Smyth
NIPS Workshop on Bayesian Deep Learning 2, 2016
462016
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
432013
Extracting sentiment networks from Shakespeare's plays
ET Nalisnick, HS Baird
2013 12th International Conference on Document Analysis and Recognition, 758-762, 2013
352013
Hybrid models with deep and invertible features
E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan
arXiv preprint arXiv:1902.02767, 2019
272019
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, 2019
252019
Infinite dimensional word embeddings
E Nalisnick, S Ravi
16*2017
A scale mixture perspective of multiplicative noise in neural networks
E Nalisnick, A Anandkumar, P Smyth
arXiv preprint arXiv:1506.03208, 2015
16*2015
Dropout as a structured shrinkage prior
E Nalisnick, JM Hernández-Lobato, P Smyth
International Conference on Machine Learning, 4712-4722, 2019
122019
Bayesian batch active learning as sparse subset approximation
R Pinsler, J Gordon, E Nalisnick, JM Hernández-Lobato
Advances in Neural Information Processing Systems, 6359-6370, 2019
122019
Learning priors for invariance
E Nalisnick, P Smyth
International Conference on Artificial Intelligence and Statistics, 366-375, 2018
72018
On priors for bayesian neural networks
ET Nalisnick
UC Irvine, 2018
72018
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
52016
Learning approximately objective priors
E Nalisnick, P Smyth
arXiv preprint arXiv:1704.01168, 2017
42017
Bayesian trees for automated cytometry data analysis
D Ji, E Nalisnick, Y Qian, RH Scheuermann, P Smyth
bioRxiv, 414904, 2018
22018
The Amortized Bootstrap
E Nalisnick, P Smyth
ICML Workshop on Implicit Models, 2017
22017
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Articles 1–20