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Didrik Nielsen
Didrik Nielsen
Head of ML @ twig.energy
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Title
Cited by
Cited by
Year
Tree boosting with xgboost-why does xgboost win" every" machine learning competition?
D Nielsen
NTNU, 2016
488*2016
Argmax flows and multinomial diffusion: Learning categorical distributions
E Hoogeboom, D Nielsen, P Jaini, P Forré, M Welling
Advances in Neural Information Processing Systems 34, 12454-12465, 2021
331*2021
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
2872018
Survae flows: Surjections to bridge the gap between vaes and flows
D Nielsen, P Jaini, E Hoogeboom, O Winther, M Welling
Advances in Neural Information Processing Systems 33, 12685-12696, 2020
1012020
Diffusion models for video prediction and infilling
T Höppe, A Mehrjou, S Bauer, D Nielsen, A Dittadi
arXiv preprint arXiv:2206.07696, 2022
892022
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 31, 2018
692018
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
592018
Few-shot diffusion models
G Giannone, D Nielsen, O Winther
arXiv preprint arXiv:2205.15463, 2022
412022
Variational adaptive-Newton method for explorative learning
ME Khan, W Lin, V Tangkaratt, Z Liu, D Nielsen
arXiv preprint arXiv:1711.05560, 2017
172017
Closing the dequantization gap: Pixelcnn as a single-layer flow
D Nielsen, O Winther
Advances in Neural Information Processing Systems 33, 3724-3734, 2020
162020
Sampling in combinatorial spaces with survae flow augmented mcmc
P Jaini, D Nielsen, M Welling
International Conference on Artificial Intelligence and Statistics, 3349-3357, 2021
102021
Argmax flows: Learning categorical distributions with normalizing flows
E Hoogeboom, D Nielsen, P Jaini, P Forré, M Welling
Third Symposium on Advances in Approximate Bayesian Inference, 2020
52020
Natural-gradient stochastic variational inference for non-conjugate structured variational autoencoder
W Lin, ME Khan, N Hubacher, D Nielsen
International Conference on Machine Learning, 2017
22017
Image classifier comprising a non-injective transformation
D Nielsen, E Hoogeboom, K Sakmann, M Welling, P Jaini
US Patent App. 17/345,702, 2022
12022
PixelCNN as a Single-Layer Flow
D Nielsen, O Winther
NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019
12019
The Variational Adaptive-Newton Method
ME Khan, W Lin, V Tangkaratt, Z Liu, D Nielsen
NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2017
12017
Image generation model based on log-likelihood
E Hoogeboom, D Nielsen, M Welling, P Forre, P Jaini, WH Beluch
US Patent 11,995,151, 2024
2024
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
P Forre, E Hoogeboom, P Jaini, D Nielsen, M Welling
15San Diego, CANeural Information Processing Systems Foundation, 2022
2022
Deep Generative Flows with Non-Bijective Layers
D Nielsen
Technical University of Denmark, 2022
2022
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