Rianne van den Berg | DeepAI

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∙ 02/01/2023

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics

Coarse-grained (CG) molecular dynamics enables the study of biological p...

0 Marloes Arts, et al. ∙

∙ 09/30/2022

Protein structure generation via folding diffusion

The ability to computationally generate novel yet physically foldable pr...

13 Kevin E. Wu, et al. ∙

∙ 09/08/2022

Clifford Neural Layers for PDE Modeling

Partial differential equations (PDEs) see widespread use in sciences and...

10 Johannes Brandstetter, et al. ∙

∙ 10/05/2021

Autoregressive Diffusion Models

We introduce Autoregressive Diffusion Models (ARDMs), a model class enco...

3 Emiel Hoogeboom, et al. ∙

∙ 07/16/2021

Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models

Denoising diffusion probabilistic models (DDPMs) have shown impressive r...

5 Daniel D. Johnson, et al. ∙

∙ 07/07/2021

Structured Denoising Diffusion Models in Discrete State-Spaces

Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have s...

19 Jacob Austin, et al. ∙

∙ 06/10/2021

Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent

We focus on the problem of domain adaptation when the goal is shifting t...

7 Samira Abnar, et al. ∙

∙ 08/03/2020

A Spectral Energy Distance for Parallel Speech Synthesis

Speech synthesis is an important practical generative modeling problem t...

3 Alexey A. Gritsenko, et al. ∙

∙ 06/22/2020

IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression

In this paper we analyse and improve integer discrete flows for lossless...

12 Rianne van den Berg, et al. ∙

∙ 06/18/2019

Differentiable probabilistic models of scientific imaging with the Fourier slice theorem

Scientific imaging techniques such as optical and electron microscopy an...

8 Karen Ullrich, et al. ∙

∙ 05/17/2019

Integer Discrete Flows and Lossless Compression

Lossless compression methods shorten the expected representation size of...

7 Emiel Hoogeboom, et al. ∙

∙ 01/30/2019

Emerging Convolutions for Generative Normalizing Flows

Generative flows are attractive because they admit exact likelihood opti...

23 Emiel Hoogeboom, et al. ∙

∙ 10/12/2018

Predictive Uncertainty through Quantization

High-risk domains require reliable confidence estimates from predictive ...

0 Bastiaan S. Veeling, et al. ∙

∙ 10/02/2018

Sinkhorn AutoEncoders

Optimal Transport offers an alternative to maximum likelihood for learni...

6 Giorgio Patrini, et al. ∙

∙ 03/15/2018

Sylvester Normalizing Flows for Variational Inference

Variational inference relies on flexible approximate posterior distribut...

0 Rianne van den Berg, et al. ∙

∙ 06/07/2017

Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of ...

0 Rianne van den Berg, et al. ∙

∙ 03/17/2017

Modeling Relational Data with Graph Convolutional Networks

Knowledge graphs enable a wide variety of applications, including questi...

0 Michael Schlichtkrull, et al. ∙

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