Didrik Nielsen

According to our database, Didrik Nielsen authored at least 11 papers between 2017 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Diffusion Models for Video Prediction and Infilling.

Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi

Trans. Mach. Learn. Res., 2022

Few-Shot Diffusion Models.

Giorgio Giannone, Didrik Nielsen, Ole Winther

CoRR, 2022

2021

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

CoRR, 2021

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC.

Priyank Jaini, Didrik Nielsen, Max Welling

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow.

Didrik Nielsen, Ole Winther

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows.

Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient.

Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models.

Mohammad Emtiyaz Khan, Didrik Nielsen

Proceedings of the International Symposium on Information Theory and Its Applications, 2018

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam.

Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Variational Adaptive-Newton Method for Explorative Learning.

Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen

CoRR, 2017