Didrik Nielsen

Computer Science · Physical Sciences

10h-index600citations17works0.02yr avg
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Research Topics

Generative Adversarial Networks and Image Synthesis(9), Gaussian Processes and Bayesian Inference(8), Machine Learning and Algorithms(4), Domain Adaptation and Few-Shot Learning(3), Music and Audio Processing(3)

Publications17 total

Diffusion Models for Video Prediction and Infilling
arXiv (Cornell University)·2022· 50 citedOpen Access
Few-Shot Diffusion Models
arXiv (Cornell University)·2022· 20 citedOpen Access
Argmax Flows and Multinomial Diffusion: Learning Categorical\n Distributions
arXiv (Cornell University)·2021· 79 citedOpen Access
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models.
arXiv (Cornell University)·2021· 25 citedOpen Access
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
arXiv (Cornell University)·2021· 36 citedOpen Access
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
arXiv (Cornell University)·2021· 1 citedOpen Access
Argmax Flows: Learning Categorical Distributions with Normalizing Flows
2020· 2 cited
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
arXiv (Cornell University)·2020· 26 citedOpen Access
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Data Archiving and Networked Services (DANS)·2020· 20 citedOpen Access
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
arXiv (Cornell University)·2020· 2 citedOpen Access
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
arXiv (Cornell University)·2018· 27 citedOpen Access
SLANG: Fast Structured Covariance Approximations for Bayesian Deep\n Learning with Natural Gradient
arXiv (Cornell University)·2018· 10 citedOpen Access
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
2018· 4 citedOpen Access
Fast yet Simple Natural-Gradient Descent for Variational Inference in\n Complex Models
arXiv (Cornell University)·2018Open Access
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
arXiv (Cornell University)·2018· 60 citedOpen Access
Variational Adaptive-Newton Method for Explorative Learning
arXiv (Cornell University)·2017· 7 citedOpen Access
Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?
BIBSYS Brage (BIBSYS (Norway))·2016· 231 citedOpen Access

Frequent Co-authors

Priyank Jaini(7), Max Welling(7), Emiel Hoogeboom(6), Mohammad Emtiyaz Khan(6), Patrick Forré(4), Ole Winther(4), Voot Tangkaratt(2), Lin Wu(2), Aaron Mishkin(2), Frederik Künstner(2), Mark Schmidt(2), Yarin Gal(1), Akash Srivastava(1), Tobias Höppe(1), Arash Mehrjou(1), Sebastian Bauer(1), Andrea Dittadi(1), Giorgio Giannone(1), Zuozhu Liu(1)