Wu Lin
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Fast and scalable bayesian deep learning by weight-perturbation in adam
ME Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava
arXiv preprint arXiv:1806.04854, 2018
902018
Conjugate-computation variational inference: Converting variational inference in non-conjugate models to inferences in conjugate models
ME Khan, W Lin
arXiv preprint arXiv:1703.04265, 2017
462017
Variational message passing with structured inference networks
W Lin, N Hubacher, ME Khan
arXiv preprint arXiv:1803.05589, 2018
302018
Faster stochastic variational inference using proximal-gradient methods with general divergence functions
ME Khan, R Babanezhad, W Lin, M Schmidt, M Sugiyama
arXiv preprint arXiv:1511.00146, 2015
272015
Variational adaptive-Newton method for explorative learning
ME Khan, W Lin, V Tangkaratt, Z Liu, D Nielsen
arXiv preprint arXiv:1711.05560, 2017
82017
Convergence of proximal-gradient stochastic variational inference under non-decreasing step-size sequence
ME Khan, R Babanezhad, W Lin, M Schmidt, M Sugiyama
arXiv preprint arXiv:1511.00146, 2015
82015
WaterlooClarke: TREC 2015 Total Recall Track.
H Zhang, W Lin, Y Wang, CLA Clarke, MD Smucker
TREC, 2015
72015
Fast and simple natural-gradient variational inference with mixture of exponential-family approximations
W Lin, ME Khan, M Schmidt
arXiv preprint arXiv:1906.02914, 2019
52019
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
W Lin, ME Khan, M Schmidt
arXiv preprint arXiv:1910.13398, 2019
12019
Natural-Gradient Stochastic Variational Inference for Non-Conjugate Structured Variational Autoencoder
W Lin, ME Khan, N Hubacher, D Nielsen
12017
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
W Lin, M Schmidt, ME Khan
arXiv preprint arXiv:2002.10060, 2020
2020
Variational Inference on Deep Exponential Family by using Variational Inferences on Conjugate Models
ME Khan, W Lin
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Makaleler 1–12