1.1k citations · 1.9k across the 23 of their papers we have counts for
24 papers
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space
Sebastian Ehlert, Jan Hermann, Thijs Vogels +11
Accurate thermochemical data with sub-chemical accuracy (within 1 kcal mol of the empirical ground truth) are essential for advancing computational chemistry methods. Howeve…
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang +6
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accur…
Protein structure generation via folding diffusion
Kevin E. Wu, Kevin K. Yang, Rianne van den Berg +3
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable disease…
Clifford Neural Layers for PDE Modeling
Johannes Brandstetter, Rianne van den Berg, Max Welling +1
Partial differential equations (PDEs) see widespread use in sciences and engineering to describe simulation of physical processes as scalar and vector fields interacting and coevol…
Autoregressive Diffusion Models
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings +3
We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete di…
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
Daniel D. Johnson, Jacob Austin, Rianne van den Berg +1
Denoising diffusion probabilistic models (DDPMs) have shown impressive results on sequence generation by iteratively corrupting each example and then learning to map corrupted vers…
Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
Samira Abnar, Rianne van den Berg, Golnaz Ghiasi +3
We focus on the problem of domain adaptation when the goal is shifting the model towards the target distribution, rather than learning domain invariant representations. It has been…
Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho +2
Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Disc…
A Spectral Energy Distance for Parallel Speech Synthesis
Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg +2
Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now…
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg, Alexey A. Gritsenko, Mostafa Dehghani +2
In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transform…
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
Karen Ullrich, Rianne van den Berg, Marcus Brubaker +2
Scientific imaging techniques such as optical and electron microscopy and computed tomography (CT) scanning are used to study the 3D structure of an object through 2D observations.…
Integer Discrete Flows and Lossless Compression
Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg +1
Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setti…
Emerging Convolutions for Generative Normalizing Flows
Emiel Hoogeboom, Rianne van den Berg, Max Welling
Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that gener…
Predictive Uncertainty through Quantization
Bastiaan S. Veeling, Rianne van den Berg, Max Welling
High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used…
Sinkhorn AutoEncoders
Giorgio Patrini, Rianne van den Berg, Patrick Forré +5
Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator…
Sylvester Normalizing Flows for Variational Inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak +1
Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce…
Integrable spin chains with random interactions
Fabian H. L. Essler, Rianne van den Berg, Vladimir Gritsev
We study a Yang-Baxter integrable quantum spin-1/2 chain with random interactions. The Hamiltonian is local and involves two and three-spin interactions with random parameters. We…
Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite…
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance,…
Atomic spin chain realization of a model for quantum criticality
R. Toskovic, R. van den Berg, A. Spinelli +5
The ability to manipulate single atoms has opened up the door to constructing interesting and useful quantum structures from the ground up. On the one hand, nanoscale arrangements…
Separation of Timescales in a Quantum Newton's Cradle
R. van den Berg, B. Wouters, S. Eliëns +3
For strongly repulsive bosons in one dimension, we provide detailed modeling of the Bragg pulse used in quantum Newton's cradle-like settings or in Bragg spectroscopy experiments.…
Competing interactions in semiconductor quantum dots
Rianne van den Berg, Giuseppe Brandino, Omar El Araby +3
We introduce an integrability-based method enabling the study of semiconductor quantum dot models incorporating both the full hyperfine interaction as well as a mean-field treatmen…
Probing pairing correlations in Sn isotopes using Richardson-Gaudin integrability
Stijn De Baerdemacker, Veerle Hellemans, Rianne van den Berg +5
Pairing correlations in the even-even A=102-130 Sn isotopes are discussed, based on the Richardson-Gaudin variables in an exact Woods-Saxon plus reduced BCS pairing framework. The…