Rianne van den Berg
Rianne van den Berg
senior researcher, Microsoft Research
Bestätigte E-Mail-Adresse bei microsoft.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Modeling Relational Data with Graph Convolutional Networks
M Schlichtkrull, TN Kipf, P Bloem, R van den Berg, I Titov, M Welling
arXiv preprint arXiv:1703.06103, 2017
15712017
Graph Convolutional Matrix Completion
R van den Berg, TN Kipf, M Welling
arXiv preprint arXiv:1706.02263, 2017
5022017
Sylvester normalizing flows for variational inference
R Berg, L Hasenclever, JM Tomczak, M Welling
arXiv preprint arXiv:1803.05649, 2018
1382018
Atomic spin-chain realization of a model for quantum criticality
R Toskovic, R van den Berg, A Spinelli, IS Eliens, B van den Toorn, ...
Nature Physics 12 (7), 656-660, 2016
902016
Emerging convolutions for generative normalizing flows
E Hoogeboom, R Van Den Berg, M Welling
International Conference on Machine Learning, 2771-2780, 2019
622019
Integer discrete flows and lossless compression
E Hoogeboom, J Peters, R van den Berg, M Welling
Advances in Neural Information Processing Systems, 12134-12144, 2019
512019
Sinkhorn autoencoders
G Patrini, R Berg, P Forré, M Carioni, S Bhargav, M Welling, T Genewein, ...
arXiv preprint arXiv:1810.01118, 2018
412018
Separation of Time Scales in a Quantum Newton’s Cradle
R van den Berg, B Wouters, S Eliëns, J De Nardis, RM Konik, JS Caux
Physical Review Letters 116 (22), 225302, 2016
392016
Competing interactions in semiconductor quantum dots
R van den Berg, GP Brandino, O El Araby, RM Konik, V Gritsev, JS Caux
Physical Review B 90 (15), 155117, 2014
132014
A spectral energy distance for parallel speech synthesis
A Gritsenko, T Salimans, R van den Berg, J Snoek, N Kalchbrenner
Advances in Neural Information Processing Systems 33, 2020
102020
Idf++: Analyzing and improving integer discrete flows for lossless compression
R van den Berg, AA Gritsenko, M Dehghani, CK Sønderby, T Salimans
International Conference on Learning Representations, 2020
8*2020
Variational inference with orthogonal normalizing flows
L Hasenclever, J Tomczak, R van den Berg, M Welling
Bayesian Deep Learning, NIPS 2017 workshop, 2017
52017
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
K Ullrich, R Berg, M Brubaker, D Fleet, M Welling
arXiv preprint arXiv:1906.07582, 2019
42019
Probing pairing correlations in Sn isotopes using Richardson-Gaudin integrability
S De Baerdemacker, V Hellemans, R van den Berg, JS Caux, K Heyde, ...
Journal of Physics: Conference Series 533 (1), 012058, 2014
32014
Structured Denoising Diffusion Models in Discrete State-Spaces
J Austin, D Johnson, J Ho, D Tarlow, R Berg
arXiv preprint arXiv:2107.03006, 2021
22021
Predictive Uncertainty through Quantization
BS Veeling, R Berg, M Welling
arXiv preprint arXiv:1810.05500, 2018
22018
Integrable spin chains with random interactions
FHL Essler, R van den Berg, V Gritsev
Physical Review B 98 (2), 024203, 2018
22018
Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent
S Abnar, R Berg, G Ghiasi, M Dehghani, N Kalchbrenner, H Sedghi
arXiv preprint arXiv:2106.06080, 2021
12021
Autoregressive Diffusion Models
E Hoogeboom, AA Gritsenko, J Bastings, B Poole, R Berg, T Salimans
arXiv preprint arXiv:2110.02037, 2021
2021
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
DD Johnson, J Austin, R van den Berg, D Tarlow
ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021
2021
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20