Joshua V. Dillon
Joshua V. Dillon
Verified email at google.com
Title
Cited by
Cited by
Year
Deep variational information bottleneck
AA Alemi, I Fischer, JV Dillon, K Murphy
arXiv preprint arXiv:1612.00410, 2016
4032016
Fixing a broken ELBO
A Alemi, B Poole, I Fischer, J Dillon, RA Saurous, K Murphy
International Conference on Machine Learning, 159-168, 2018
233*2018
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, 13991-14002, 2019
1482019
Tensorflow distributions
JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ...
arXiv preprint arXiv:1711.10604, 2017
1112017
The locally weighted bag of words framework for document representation
G Lebanon, Y Mao, J Dillon
Journal of Machine Learning Research 8 (Oct), 2405-2441, 2007
812007
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, 14707-14718, 2019
552019
Sequential document visualization
Y Mao, J Dillon, G Lebanon
IEEE transactions on visualization and computer graphics 13 (6), 1208-1215, 2007
462007
Uncertainty in the variational information bottleneck
AA Alemi, I Fischer, JV Dillon
arXiv preprint arXiv:1807.00906, 2018
302018
Stochastic composite likelihood
JV Dillon, G Lebanon
The Journal of Machine Learning Research 11, 2597-2633, 2010
272010
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
272010
Statistical translation, heat kernels and expected distances
J Dillon, Y Mao, G Lebanon, J Zhang
arXiv preprint arXiv:1206.5248, 2012
262012
Statistical and computational tradeoffs in stochastic composite likelihood
J Dillon, G Lebanon
Artificial Intelligence and Statistics, 129-136, 2009
172009
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
152019
Asymptotic analysis of generative semi-supervised learning
JV Dillon, K Balasubramanian, G Lebanon
arXiv preprint arXiv:1003.0024, 2010
142010
Deep variational information bottleneck. arXiv 2016
AA Alemi, I Fischer, JV Dillon, K Murphy
arXiv preprint arXiv:1612.00410, 0
12
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
42020
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
12020
Joint Distributions for TensorFlow Probability
D Piponi, D Moore, JV Dillon
arXiv preprint arXiv:2001.11819, 2020
12020
Quadrature Compound: An approximating family of distributions
J Dillon, I Langmore
Arxiv, 2018
12018
Tensorflow distributions
A Alemi, B Patton, D Moore, D Tran, E Brevdo, I Langmore, J Dillon, ...
12017
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