Benjamin Kurt Miller

According to our database, Benjamin Kurt Miller authored at least 17 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Adjoint Schrödinger Bridge Sampler.

Guan-Horng Liu, Jaemoo Choi, Yongxin Chen, Benjamin Kurt Miller, Ricky T. Q. Chen

CoRR, June, 2025

sbi reloaded: a toolkit for simulation-based inference workflows.

Jan Boelts, Michael Deistler, Manuel Glöckler, Álvaro Tejero-Cantero, Jan-Matthis Lueckmann, Guy Moss, Peter Steinbach, Thomas Moreau, Fabio Muratore, Julia Linhart, Conor Durkan, Julius Vetter, Benjamin Kurt Miller, Maternus Herold, Abolfazl Ziaeemehr, Matthijs Pals, Theo Gruner, Sebastian Bischoff, Nastya Krouglova, Richard Gao, Janne K. Lappalainen, Bálint Mucsányi, Felix Pei, Auguste Schulz, Zinovia Stefanidi, Pedro Rodrigues, Cornelius Schröder, Faried Abu Zaid, Jonas Beck, Jaivardhan Kapoor, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke

J. Open Source Softw., May, 2025

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching.

Aaron J. Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Brandon M. Wood, Daniel Levine, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen

CoRR, April, 2025

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials.

Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi

CoRR, March, 2025

2024

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions.

Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

FlowMM: Generating Materials with Riemannian Flow Matching.

Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Simulation-based Inference with the Generalized Kullback-Leibler Divergence.

Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré

CoRR, 2023

Balancing Simulation-based Inference for Conservative Posteriors.

Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe

CoRR, 2023

2022

swyft: Truncated Marginal Neural Ratio Estimation in Python.

Benjamin Kurt Miller, Alex Cole, Christoph Weniger, Francesco Nattino, Ou Ku, Meiert W. Grootes

J. Open Source Softw., 2022

Contrastive Neural Ratio Estimation.

Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Generative Coarse-Graining of Molecular Conformations.

Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli

Proceedings of the International Conference on Machine Learning, 2022

2021

Automatically detecting anomalous exoplanet transits.

Christoph J. Hönes, Benjamin Kurt Miller, Ana M. Heras, Bernard H. Foing

CoRR, 2021

Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation.

Alex Cole, Benjamin Kurt Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, Christoph Weniger

CoRR, 2021

Truncated Marginal Neural Ratio Estimation.

Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time.

Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger

CoRR, 2020

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties.

Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

CoRR, 2020

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks.

Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller

CoRR, 2020