Fetching the paper…
Reading the bibliography…
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions.
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 1903
Earlier work this paper cites.
Neural stochastic differential equations: Deep latent Gaussian models in the diffusion limit
Belinda Tzen and Maxim Raginsky · 1905
Earlier work this paper cites.
Dynamic programming
Richard Bellman · 1957
Earlier work this paper cites.
Applied Optimal Control
A. E. Bryson and Y. C. Ho · 1969
Earlier work this paper cites.
Stochastic control and nonequilibrium thermodynamical systems
Michele Pavon · 1989
Earlier work this paper cites.
A stochastic control approach to reciprocal diffusion processes
Paolo Dai Pra · 1991
Earlier work this paper cites.
Annealed importance sampling
Radford M Neal · 2001
Earlier work this paper cites.
Molecular properties that influence the oral bioavailability of drug candidates
Daniel F Veber, Stephen R Johnson, Hung-Yuan Cheng, Brian R Smith, Keith W Ward, and Kenneth D Kopple · 2002
Earlier work this paper cites.
An entropy approach to the time reversal of diffusion processes
Hans Föllmer · 2005
Earlier work this paper cites.
Path integrals and symmetry breaking for optimal control theory
H J Kappen · 2005
Earlier work this paper cites.
Stochastic differential equations
Philip E Protter and Philip E Protter · 2005
Earlier work this paper cites.
Sequential monte carlo samplers
Pierre Del Moral, Arnaud Doucet, and Ajay Jasra · 2006
Earlier work this paper cites.
MCMC using Hamiltonian dynamics
Radford M Neal et al · 2011
Earlier work this paper cites.
Deterministic and Stochastic Optimal Control
W.H. Fleming and R.W. Rishel · 2012
Earlier work this paper cites.
The orca program system
F. Neese · 2012
Earlier work this paper cites.
Rdkit documentation
Greg Landrum · 2013
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Better informed distance geometry: using what we know to improve conformation generation
Sereina Riniker and Gregory A Landrum · 2015
Earlier work this paper cites.
On the relation between optimal transport and schrödinger bridges: A stochastic control viewpoint
Yongxin Chen, Tryphon T Georgiou, and Michele Pavon · 2016
Earlier work this paper cites.
A robust and accurate tight-binding quantum chemical method for structures, vibrational frequencies, and noncovalent interactions of large molecular systems parametrized for all spd-block elements (z= 1–86)
Stefan Grimme, Christoph Bannwarth, and Philip Shushkov · 2017
Earlier work this paper cites.
Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Clebsch-gordan nets: a fully fourier space spherical convolutional neural network, 2018
Risi Kondor, Zhen Lin, and Shubhendu Trivedi · 2018
Earlier work this paper cites.
Optimal Control Theory: Applications to Management Science and Economics
S.P. Sethi · 2018
Earlier work this paper cites.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Earlier work this paper cites.
3d steerable cnns: Learning rotationally equivariant features in volumetric data, 2018
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
Earlier work this paper cites.
Path integral sampler: A stochastic control approach for sampling
Qinsheng Zhang and Yongxin Chen · 2018
Earlier work this paper cites.
Flow-based generative models for markov chain monte carlo in lattice field theory
Michael S Albergo, Gurtej Kanwar, and Phiala E Shanahan · 2019
Earlier work this paper cites.
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Equivariant flows: exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
Cited alongside, same era.
Relevance of rotationally equivariant convolutions for predicting molecular properties
Benjamin Kurt Miller, Mario Geiger, Tess E Smidt, and Frank Noé · 2020
Cited alongside, same era.
Automated exploration of the low-energy chemical space with fast quantum chemical methods
Philipp Pracht, Fabian Bohle, and Stefan Grimme · 2020
Cited alongside, same era.
A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-Garcıa, Léna Néhale Ezzine, Yoshua Bengio, and Nikolay Malkin · 2023
Later among the works it cites.
Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
Later among the works it cites.
Trajectory balance: Improved credit assignment in gflownets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, and Yoshua Bengio · 2023
Later among the works it cites.
Flow annealed importance sampling bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor NC Simm, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2023
Later among the works it cites.
Improved sampling via learned diffusions
Lorenz Richter and Julius Berner · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Stochastic normalizing flows
Hao Wu, Jonas Köhler, and Frank Noé · 2020
Cited alongside, same era.
Annealed flow transport monte carlo
Michael Arbel, Alex Matthews, and Arnaud Doucet · 2021
Cited alongside, same era.
Geomol: Torsional geometric generation of molecular 3d conformer ensembles
Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen, William Green, and Tommi Jaakkola · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
Geom, energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
Cited alongside, same era.
Mace: Higher order equivariant message passing neural networks for fast and accurate force fields
Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi · 2022
Cited alongside, same era.
Diffusion schrödinger bridge matching
Yuyang Shi, Valentin De Bortoli, Andrew Campbell, and Arnaud Doucet · 2023
Later among the works it cites.
Aligned diffusion schrödinger bridges
Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, and Charlotte Bunne · 2023
Later among the works it cites.
Denoising diffusion samplers
Francisco Vargas, Will Sussman Grathwohl, and Arnaud Doucet · 2023
Later among the works it cites.
Iterated denoising energy matching for sampling from boltzmann densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, et al · 2024
Later among the works it cites.
Nets: A non-equilibrium transport sampler
Michael S Albergo and Eric Vanden-Eijnden · 2024
Later among the works it cites.
Valentin De Bortoli, Michael Hutchinson, Peter Wirnsberger, and Arnaud Doucet · 2024
Later among the works it cites.
Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics
Juan Viguera Diez, Sara Romeo Atance, Ola Engkvist, and Simon Olsson · 2024
Later among the works it cites.
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, and Ricky T. Q. Chen · 2024
Later among the works it cites.
dxtb—an efficient and fully differentiable framework for extended tight-binding
Marvin Friede, Christian Hölzer, Sebastian Ehlert, and Stefan Grimme · 2024
Later among the works it cites.
Et-flow: Equivariant flow-matching for molecular conformer generation
Majdi Hassan, Nikhil Shenoy, Jungyoon Lee, Hannes Stark, Stephan Thaler, and Dominique Beaini · 2024
Later among the works it cites.
A simulation-free deep learning approach to stochastic optimal control
Mengjian Hua, Matthieu Laurière, and Eric Vanden-Eijnden · 2024
Later among the works it cites.
Generalized schrödinger bridge matching
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos Theodorou, and Ricky T. Q. Chen · 2024
Later among the works it cites.
Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
Later among the works it cites.
SE(3) equivariant augmented coupling flows
Laurence Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2024
Later among the works it cites.
Particle denoising diffusion sampler
Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2024
Later among the works it cites.
Crest—a program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, Christoph Bannwarth, Fabian Bohle, Sebastian Ehlert, Gereon Feldmann, Johannes Gorges, Marcel Müller, Tim Neudecker, Christoph Plett, et al · 2024
Later among the works it cites.
Improved sampling via learned diffusions
Lorenz Richter and Julius Berner · 2024
Later among the works it cites.
Transport meets variational inference: Controlled monte carlo diffusions
Francisco Vargas, Shreyas Padhy, Denis Blessing, and Nikolas Nusken · 2024
Later among the works it cites.
Diffusion generative flow samplers: Improving learning signals through partial trajectory optimization
Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, and Yoshua Bengio · 2024
Later among the works it cites.
Learning smooth and expressive interatomic potentials for physical property prediction
Xiang Fu, Brandon M Wood, Luis Barroso-Luque, Daniel S Levine, Meng Gao, Misko Dzamba, and C Lawrence Zitnick · 2025
Closest in time.
No trick, no treat: Pursuits and challenges towards simulation-free training of neural samplers
Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, RuiKang OuYang, Carla Gomes, and José Miguel Hernández-Lobato · 2025
Closest in time.
LEAPS: A discrete neural sampler via locally equivariant networks
Peter Holderrieth, Michael S Albergo, and Tommi Jaakkola · 2025
Closest in time.