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Joshua V. Dillon
Joshua V. Dillon
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Title
Cited by
Cited by
Year
Deep variational information bottleneck
AA Alemi, I Fischer, JV Dillon, K Murphy
arXiv preprint arXiv:1612.00410, 2016
10962016
Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, J Dillon, ...
Advances in neural information processing systems 32, 2019
9432019
Fixing a broken ELBO
A Alemi, B Poole, I Fischer, J Dillon, RA Saurous, K Murphy
International Conference on Machine Learning, 159-168, 2018
465*2018
Likelihood ratios for out-of-distribution detection
J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ...
Advances in neural information processing systems 32, 2019
4282019
Tensorflow distributions
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 2017
3852017
The Locally Weighted Bag of Words Framework for Document Representation.
G Lebanon, Y Mao, J Dillon
Journal of Machine Learning Research 8 (10), 2007
932007
Neutra-lizing bad geometry in hamiltonian monte carlo using neural transport
M Hoffman, P Sountsov, JV Dillon, I Langmore, D Tran, S Vasudevan
arXiv preprint arXiv:1903.03704, 2019
742019
Uncertainty in the variational information bottleneck
AA Alemi, I Fischer, JV Dillon
arXiv preprint arXiv:1807.00906, 2018
732018
Density of states estimation for out of distribution detection
W Morningstar, C Ham, A Gallagher, B Lakshminarayanan, A Alemi, ...
International Conference on Artificial Intelligence and Statistics, 3232-3240, 2021
512021
Sequential document visualization
Y Mao, J Dillon, G Lebanon
IEEE transactions on visualization and computer graphics 13 (6), 1208-1215, 2007
492007
Can you trust your model’s uncertainty
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
evaluating predictive uncertainty under dataset shift, 2019
382019
Stochastic composite likelihood
JV Dillon, G Lebanon
The Journal of Machine Learning Research 11, 2597-2633, 2010
332010
The k-tied normal distribution: A compact parameterization of Gaussian mean field posteriors in Bayesian neural networks
J Swiatkowski, K Roth, B Veeling, L Tran, J Dillon, J Snoek, S Mandt, ...
International Conference on Machine Learning, 9289-9299, 2020
302020
Hydra: Preserving ensemble diversity for model distillation
L Tran, BS Veeling, K Roth, J Swiatkowski, JV Dillon, J Snoek, S Mandt, ...
arXiv preprint arXiv:2001.04694, 2020
292020
A unified optimization framework for robust pseudo-relevance feedback algorithms
JV Dillon, K Collins-Thompson
Proceedings of the 19th ACM international conference on Information and …, 2010
282010
Statistical translation, heat kernels and expected distances
J Dillon, Y Mao, G Lebanon, J Zhang
arXiv preprint arXiv:1206.5248, 2012
262012
Deep variational information bottleneck. arXiv 2016
AA Alemi, I Fischer, JV Dillon, K Murphy
arXiv preprint arXiv:1612.00410, 0
26
tfp. mcmc: Modern Markov chain Monte Carlo tools built for modern hardware
J Lao, C Suter, I Langmore, C Chimisov, A Saxena, P Sountsov, D Moore, ...
arXiv preprint arXiv:2002.01184, 2020
252020
Asymptotic analysis of generative semi-supervised learning
JV Dillon, K Balasubramanian, G Lebanon
arXiv preprint arXiv:1003.0024, 2010
172010
Statistical and computational tradeoffs in stochastic composite likelihood
J Dillon, G Lebanon
Artificial Intelligence and Statistics, 129-136, 2009
172009
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Articles 1–20