Obserwuj
Yingzhen Li
Tytuł
Cytowane przez
Cytowane przez
Rok
Variational Continual Learning
CV Nguyen, Y Li, TD Bui, RE Turner
5892018
Variational Continual Learning
CV Nguyen, Y Li, TD Bui, RE Turner
arXiv preprint arXiv:1710.10628, 2017
5892017
Rényi divergence variational inference
Y Li, RE Turner
Advances in Neural Information Processing Systems, 1073-1081, 2016
2592016
Deep gaussian processes for regression using approximate expectation propagation
T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner
International Conference on Machine Learning, 1472-1481, 2016
2312016
Disentangled Sequential Autoencoder
L Yingzhen, S Mandt
International Conference on Machine Learning, 5656-5665, 2018
230*2018
Black-box α-divergence minimization
JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, TD Bui, ...
International Machine Learning Society, 2016
2302016
Dropout inference in bayesian neural networks with alpha-divergences
Y Li, Y Gal
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
2052017
Stochastic expectation propagation
Y Li, JM Hernández-Lobato, RE Turner
Advances in Neural Information Processing Systems, 2323-2331, 2015
1362015
Generalization in reinforcement learning with selective noise injection and information bottleneck
M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann
Advances in Neural Information Processing Systems, 13978-13990, 2019
1192019
Are generative classifiers more robust to adversarial attacks?
Y Li, J Bradshaw, Y Sharma
International Conference on Machine Learning, 3804-3814, 2019
812019
Gradient Estimators for Implicit Models
Y Li, RE Turner
arXiv preprint arXiv:1705.07107, 2017
812017
'In-Between'Uncertainty in Bayesian Neural Networks
AYK Foong, Y Li, JM Hernández-Lobato, RE Turner
arXiv preprint arXiv:1906.11537, 2019
802019
On the expressiveness of approximate inference in bayesian neural networks
A Foong, D Burt, Y Li, R Turner
Advances in Neural Information Processing Systems 33, 2020
772020
Approximate Inference with Amortised MCMC
Y Li, RE Turner, Q Liu
arXiv preprint arXiv:1702.08343, 2017
572017
Variational implicit processes
C Ma, Y Li, JM Hernández-Lobato
International Conference on Machine Learning, 4222-4233, 2019
542019
A causal view on robustness of neural networks
C Zhang, K Zhang, Y Li
Advances in Neural Information Processing Systems 33, 289-301, 2020
492020
Meta-Learning for Stochastic Gradient MCMC
W Gong, Y Li, JM Hernández-Lobato
arXiv preprint arXiv:1806.04522, 2018
392018
Sliced Kernelized Stein Discrepancy
W Gong, Y Li, JM Hernández-Lobato
arXiv preprint arXiv:2006.16531, 2020
302020
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
S Lunz, Y Li, A Fitzgibbon, N Kushman
arXiv preprint arXiv:2002.12674, 2020
292020
On the importance of the Kullback-Leibler divergence term in variational autoencoders for text generation
V Prokhorov, E Shareghi, Y Li, MT Pilehvar, N Collier
arXiv preprint arXiv:1909.13668, 2019
252019
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