Didrik Nielsen

Publication Activity (10 Years)

Years Active: 2017-2022
Publications (10 Years): 18

Top Topics

Metropolis Hastings

Variational Inference

Diffusion Models

Top Venues

Publications

Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi
Diffusion Models for Video Prediction and Infilling. Trans. Mach. Learn. Res. 2022 (2022)
Tobias Höppe, Arash Mehrjou, Stefan Bauer, Didrik Nielsen, Andrea Dittadi
Diffusion Models for Video Prediction and Infilling. CoRR (2022)
Giorgio Giannone, Didrik Nielsen, Ole Winther
Few-Shot Diffusion Models. CoRR (2022)
Priyank Jaini, Didrik Nielsen, Max Welling
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. CoRR (2021)
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. CoRR (2021)
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS (2021)
Priyank Jaini, Didrik Nielsen, Max Welling
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. AISTATS (2021)
Didrik Nielsen, Ole Winther
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. NeurIPS (2020)
Didrik Nielsen, Ole Winther
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. CoRR (2020)
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. CoRR (2020)
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS (2020)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR (2018)
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. ISITA (2018)
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. CoRR (2018)
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen
Variational Adaptive-Newton Method for Explorative Learning. CoRR (2017)