| Tree boosting with xgboost-why does xgboost win" every" machine learning competition? D Nielsen NTNU, 2016 | 131* | 2016 |
| 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 | 76 | 2018 |
| 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, 6245-6255, 2018 | 17 | 2018 |
| 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 | 16 | 2018 |
| Variational adaptive-Newton method for explorative learning ME Khan, W Lin, V Tangkaratt, Z Liu, D Nielsen arXiv preprint arXiv:1711.05560, 2017 | 6 | 2017 |
| SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows D Nielsen, P Jaini, E Hoogeboom, O Winther, M Welling arXiv preprint arXiv:2007.02731, 2020 | 1 | 2020 |
| Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow D Nielsen, O Winther arXiv preprint arXiv:2002.02547, 2020 | 1 | 2020 |
| Natural-Gradient Stochastic Variational Inference for Non-Conjugate Structured Variational Autoencoder W Lin, ME Khan, N Hubacher, D Nielsen | 1 | 2017 |
| PixelCNN as a Single-Layer Flow D Nielsen, O Winther | | |
| The Variational Adaptive-Newton Method ME Khan, W Lin, V Tangkaratt, Z Liu, D Nielsen | | |
