Seuraa
Daniel M. Roy
Daniel M. Roy
Research Director, Vector Institute; Prof., U. Toronto (Statistics, CS)
Vahvistettu sähköpostiosoite verkkotunnuksessa utoronto.ca - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Church: a language for generative models
ND Goodman, VK Mansinghka, D Roy, K Bonawitz, JB Tenenbaum
Uncertainty in Artificial Intelligence 22, 23, 2008
1018*2008
Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
GK Dziugaite, DM Roy
arXiv preprint arXiv:1703.11008, 2017
8712017
Training generative neural networks via maximum mean discrepancy optimization
GK Dziugaite, DM Roy, Z Ghahramani
arXiv preprint arXiv:1505.03906, 2015
6782015
A study of the effect of jpg compression on adversarial images
GK Dziugaite, Z Ghahramani, DM Roy
arXiv preprint arXiv:1608.00853, 2016
6252016
Linear mode connectivity and the lottery ticket hypothesis
J Frankle, GK Dziugaite, D Roy, M Carbin
International Conference on Machine Learning, 3259-3269, 2020
5922020
Enhancing server availability and security through failure-oblivious computing
M Rinard, C Cadar, D Dumitran, DM Roy, T Leu, WS Beebee Jr
Proceedings of the 6th conference on Symposium on Opearting Systems Design …, 2004
4752004
Bayesian models of graphs, arrays and other exchangeable random structures
P Orbanz, DM Roy
IEEE transactions on pattern analysis and machine intelligence 37 (2), 437-461, 2014
3142014
Stabilizing the lottery ticket hypothesis
J Frankle, GK Dziugaite, DM Roy, M Carbin
arXiv preprint arXiv:1903.01611, 2019
3012019
Mondrian forests: Efficient online random forests
B Lakshminarayanan, DM Roy, YW Teh
Advances in neural information processing systems 27, 2014
2962014
Pruning neural networks at initialization: Why are we missing the mark?
J Frankle, GK Dziugaite, DM Roy, M Carbin
arXiv preprint arXiv:2009.08576, 2020
2582020
The Mondrian process
DM Roy, YW Teh
Adv. in Neural Inform. Processing Syst 21, 27, 2009
2182009
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel
S Fort, GK Dziugaite, M Paul, S Kharaghani, DM Roy, S Ganguli
Advances in Neural Information Processing Systems 33, 5850-5861, 2020
2002020
Neural network matrix factorization
GK Dziugaite, DM Roy
arXiv preprint arXiv:1511.06443, 2015
1842015
Data-dependent PAC-Bayes priors via differential privacy
GK Dziugaite, DM Roy
Advances in neural information processing systems 31, 2018
1642018
Information-theoretic generalization bounds for SGLD via data-dependent estimates
J Negrea, M Haghifam, GK Dziugaite, A Khisti, DM Roy
Advances in Neural Information Processing Systems 32, 2019
1602019
Complexity of Inference in Latent Dirichlet Allocation
D Sontag, DM Roy
153*
Random function priors for exchangeable arrays with applications to graphs and relational data
J Lloyd, P Orbanz, Z Ghahramani, DM Roy
Advances in Neural Information Processing Systems 25, 2012
1442012
Bayesian agglomerative clustering with coalescents
YW Teh, H Daumé III, D Roy
Arxiv preprint arXiv:0907.0781, 2009
1422009
A dynamic technique for eliminating buffer overflow vulnerabilities (and other memory errors)
M Rinard, C Cadar, D Dumitran, DM Roy, T Leu
20th Annual Computer Security Applications Conference, 82-90, 2004
1422004
Probabilistically accurate program transformations
S Misailovic, D Roy, M Rinard
Static Analysis, 316-333, 2011
1222011
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Artikkelit 1–20