59 citations · 240 across the 11 of their papers we have counts for
11 papers
AI/ML for 5G and Beyond Cybersecurity
Sandeep Pirbhulal, Habtamu Abie, Martin Jullum +2
The advancements in communication technology (5G and beyond) and global connectivity Internet of Things (IoT) also come with new security problems that will need to be addressed in…
Diffusion Models for Video Prediction and Infilling
Tobias Höppe, Arash Mehrjou, Stefan Bauer +2
Predicting and anticipating future outcomes or reasoning about missing information in a sequence are critical skills for agents to be able to make intelligent decisions. This requi…
Few-Shot Diffusion Models
Giorgio Giannone, Didrik Nielsen, Ole Winther
Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties…
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
Priyank Jaini, Didrik Nielsen, Max Welling
Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions. However, a major limitation of HMC is its inability to be appli…
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini +2
Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural images. This paper introduces two extensions of flows and diffusion for c…
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom +2
Normalizing flows and variational autoencoders are powerful generative models that can represent complicated density functions. However, they both impose constraints on the models:…
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Didrik Nielsen, Ole Winther
Flow models have recently made great progress at modeling ordinal discrete data such as images and audio. Due to the continuous nature of flow models, dequantization is typically a…
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen +2
Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distributio…
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
Mohammad Emtiyaz Khan, Didrik Nielsen
Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to complex models such as deep neural networks. Variationa…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…
Variational Adaptive-Newton Method for Explorative Learning
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt +2
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and rein…