Variational Adaptive-Newton Method for Explorative Learning. - Mohammad Emtiyaz Khan; Wu Lin; Voot Tangkaratt; Zuozhu Liu; Didrik Nielsen (CoRR, 2017)

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. - Mohammad Emtiyaz Khan; Didrik Nielsen; Voot Tangkaratt; Wu Lin; Yarin Gal; Akash Srivastava (ICML, 2018)

Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models. - Mohammad Emtiyaz Khan; Didrik Nielsen (ISITA, 2018)

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. - Aaron Mishkin; Frederik Kunstner; Didrik Nielsen; Mark Schmidt 001; Mohammad Emtiyaz Khan (NeurIPS, 2018)

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. - Didrik Nielsen; Priyank Jaini; Emiel Hoogeboom; Ole Winther; Max Welling (NeurIPS, 2020)

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow. - Didrik Nielsen; Ole Winther (NeurIPS, 2020)

Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. - Priyank Jaini; Didrik Nielsen; Max Welling (AISTATS, 2021)

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. - Emiel Hoogeboom; Didrik Nielsen; Priyank Jaini; Patrick Forré; Max Welling (NeurIPS, 2021)

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. - Emiel Hoogeboom; Didrik Nielsen; Priyank Jaini; Patrick Forré; Max Welling (CoRR, 2021)

Few-Shot Diffusion Models. - Giorgio Giannone; Didrik Nielsen; Ole Winther (CoRR, 2022)

Diffusion Models for Video Prediction and Infilling. - Tobias Höppe; Arash Mehrjou; Stefan Bauer; Didrik Nielsen; Andrea Dittadi (Trans. Mach. Learn. Res., 2022)

AI/ML for 5G and Beyond Cybersecurity. - Sandeep Pirbhulal; Habtamu Abie; Martin Jullum; Didrik Nielsen; Anders Løland (CoRR, 2025)