| Fast and scalable bayesian deep learning by weight-perturbation in adam M Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava International Conference on Machine Learning, 2611-2620, 2018 | 249 | 2018 |
| Practical Deep Learning with Bayesian Principles K Osawa, S Swaroop, A Jain, R Eschenhagen, RE Turner, R Yokota, ... arXiv preprint arXiv:1906.02506, 2019 | 216 | 2019 |
| AI for social good: unlocking the opportunity for positive impact N Tomašev, J Cornebise, F Hutter, S Mohamed, A Picciariello, B Connelly, ... Nature Communications 11 (1), 2468, 2020 | 168 | 2020 |
| Conjugate-computation variational inference: Converting variational inference in non-conjugate models to inferences in conjugate models M Khan, W Lin Artificial Intelligence and Statistics, 878-887, 2017 | 126 | 2017 |
| 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 | 114 | 2017 |
| Continual deep learning by functional regularisation of memorable past P Pan, S Swaroop, A Immer, R Eschenhagen, R Turner, MEE Khan Advances in Neural Information Processing Systems 33, 4453-4464, 2020 | 99 | 2020 |
| Approximate Inference Turns Deep Networks into Gaussian Processes MEE Khan, A Immer, E Abedi, M Korzepa Advances in Neural Information Processing Systems, 3088-3098, 2019 | 89 | 2019 |
| Variational bounds for mixed-data factor analysis MEE Khan, G Bouchard, KP Murphy, BM Marlin Advances in Neural Information Processing Systems 23, 1108-1116, 2010 | 81 | 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 | 72 | 2007 |
| Scalable marginal likelihood estimation for model selection in deep learning A Immer, M Bauer, V Fortuin, G Rätsch, KM Emtiyaz International Conference on Machine Learning, 4563-4573, 2021 | 67 | 2021 |
| 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 | 58 | 2018 |
| A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. ME Khan, S Mohamed, BM Marlin, KP Murphy AISTATS, 610-618, 2012 | 57 | 2012 |
| 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 | 54 | 2018 |
| Fast and simple natural-gradient variational inference with mixture of exponential-family approximations W Lin, ME Khan, M Schmidt International Conference on Machine Learning, 3992-4002, 2019 | 47 | 2019 |
| Variational Message Passing with Structured Inference Networks W Lin, N Hubacher, ME Khan arXiv preprint arXiv:1803.05589, 2018 | 46 | 2018 |
| Kullback-Leibler Proximal Variational Inference ME Khan, P Baqué, F Fleuret, P Fua Advances in Neural Information Processing Systems, 2015 | 44 | 2015 |
| The Bayesian Learning Rule ME Khan, H Rue arXiv preprint arXiv:2107.04562, 2021 | 43 | 2021 |
| TD-Regularized Actor-Critic Methods S Parisi, V Tangkaratt, J Peters, ME Khan arXiv preprint arXiv:1812.08288, 2018 | 40 | 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 | 40 | 2016 |
| Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. BM Marlin, ME Khan, KP Murphy ICML, 633-640, 2011 | 40 | 2011 |
Wu LinVector InstituteVerified email at vectorinstitute.ai
