Mohammad Emtiyaz Khan
Mohammad Emtiyaz Khan
Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo
Verified email at - Homepage
Cited by
Cited by
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
Smarper: Context-aware and automatic runtime-permissions for mobile devices
K Olejnik, I Dacosta, JS Machado, K Huguenin, ME Khan, JP Hubaux
2017 IEEE Symposium on Security and Privacy (SP), 1058-1076, 2017
Variational bounds for mixed-data factor analysis
ME Khan, G Bouchard, KP Murphy, BM Marlin
Advances in Neural Information Processing Systems, 1108-1116, 2010
An expectation-maximization algorithm based Kalman smoother approach for event-related desynchronization (ERD) estimation from EEG
ME Khan, DN Dutt
IEEE transactions on biomedical engineering 54 (7), 1191-1198, 2007
Practical Deep Learning with Bayesian Principles
K Osawa, S Swaroop, A Jain, R Eschenhagen, RE Turner, R Yokota, ...
arXiv preprint arXiv:1906.02506, 2019
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models.
ME Khan, S Mohamed, BM Marlin, KP Murphy
AISTATS, 610-618, 2012
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
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models.
BM Marlin, ME Khan, KP Murphy
ICML, 633-640, 2011
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models
ME Khan, A Aravkin, M Friedlander, M Seeger
International conference on Machine learning, 2013
Kullback-Leibler Proximal Variational Inference
ME Khan, P Baqué, F Fleuret, P Fua
Advances in Neural Information Processing Systems, 2015
Fast Bayesian inference for non-conjugate Gaussian process regression
E Khan, S Mohamed, KP Murphy
Advances in Neural Information Processing Systems, 3140-3148, 2012
Variational Message Passing with Structured Inference Networks
W Lin, N Hubacher, ME Khan
arXiv preprint arXiv:1803.05589, 2018
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions
ME Khan, L Switzerland, R Babanezhad, W Lin, M Schmidt, M Sugiyama
Uncertainty in Artificial Intelligence (UAI), 2016
Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient
A Mishkin, F Kunstner, D Nielsen, M Schmidt, ME Khan
Advances in Neural Information Processing Systems, 6248-6258, 2018
Accelerating Bayesian structural inference for non-decomposable Gaussian graphical models
B Moghaddam, E Khan, KP Murphy, BM Marlin
Advances in Neural Information Processing Systems, 1285-1293, 2009
UAVs using Bayesian Optimization to Locate WiFi Devices
M Carpin, S Rosati, ME Khan, B Rimoldi
arXiv preprint arXiv:1510.03592, 2015
Fast yet simple natural-gradient descent for variational inference in complex models
ME Khan, D Nielsen
2018 International Symposium on Information Theory and Its Applications …, 2018
Variational learning for latent Gaussian model of discrete data
M Khan
University of British Columbia, 2012
TD-Regularized Actor-Critic Methods
S Parisi, V Tangkaratt, J Peters, ME Khan
arXiv preprint arXiv:1812.08288, 2018
Bayesian Inference
ME Khan
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