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Paper Citation Record · LEDGER

Leveraging generative models to assist Monte Carlo sampling

As of 11 August 2026, this Paper Citation Record lists 100 of 201 outbound references and 0 inbound Pith citation observations for arXiv:2608.07648.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.07648 v1

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measured 100 of 201 reference resolution

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Reference resolution

100 of 201 outbound references displayed

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External citation measurements

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Outbound references

Observation 2494f53b-a29b-4938-a273-201c23c8ae53 · outbound

This paper cites Communications in Mathematical Sciences , volume =.

Leveraging generative models to assist Monte Carlo sampling Communications in Mathematical Sciences , volume =

Reference 1

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Leveraging generative models to assist Monte Carlo sampling Normalizing

Reference 2

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Leveraging generative models to assist Monte Carlo sampling and Brubaker, Marcus A

Reference 3

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Leveraging generative models to assist Monte Carlo sampling Density Estimation Using

Reference 4

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 5

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This paper cites Communications on Pure and Applied Mathematics , volume =.

Leveraging generative models to assist Monte Carlo sampling Communications on Pure and Applied Mathematics , volume =

Reference 6

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This paper cites Equivariant.

Leveraging generative models to assist Monte Carlo sampling Equivariant

Reference 8

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This paper cites Advances in Neural Information Processing Systems , volume =.

Leveraging generative models to assist Monte Carlo sampling Advances in Neural Information Processing Systems , volume =

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Leveraging generative models to assist Monte Carlo sampling e3nn: Euclidean Neural Networks

Reference 11

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Leveraging generative models to assist Monte Carlo sampling Proceedings of the 32nd

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

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Leveraging generative models to assist Monte Carlo sampling Denoising

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Leveraging generative models to assist Monte Carlo sampling Stochastic Processes and their Applications , volume =

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Leveraging generative models to assist Monte Carlo sampling Building

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

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Leveraging generative models to assist Monte Carlo sampling Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

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Leveraging generative models to assist Monte Carlo sampling Boltzmann Generators:

Reference 21

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

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Leveraging generative models to assist Monte Carlo sampling Asymptotically unbiased estimation of physical observables with neural samplers

Reference 23

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Leveraging generative models to assist Monte Carlo sampling and Anders, Christopher J

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Leveraging generative models to assist Monte Carlo sampling Physical Review E , volume =

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Leveraging generative models to assist Monte Carlo sampling Efficient Estimation of Free Energy Differences from

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Leveraging generative models to assist Monte Carlo sampling Normalizing

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Leveraging generative models to assist Monte Carlo sampling The Journal of Chemical Physics , volume =

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Leveraging generative models to assist Monte Carlo sampling Normalizing flows for atomic solids

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Leveraging generative models to assist Monte Carlo sampling Estimating

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Leveraging generative models to assist Monte Carlo sampling Scalable

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Leveraging generative models to assist Monte Carlo sampling doi:10.48550/arXiv.2512.23930 , archiveprefix =

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Leveraging generative models to assist Monte Carlo sampling Estimating

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Leveraging generative models to assist Monte Carlo sampling and Shirts, Michael R

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Leveraging generative models to assist Monte Carlo sampling Particle

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Leveraging generative models to assist Monte Carlo sampling Performance of Machine-Learning-Assisted

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Leveraging generative models to assist Monte Carlo sampling NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

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Leveraging generative models to assist Monte Carlo sampling Transport

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Leveraging generative models to assist Monte Carlo sampling and Marzouk, Youssef M

Reference 42

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Observation 71e14b85-f8a4-4724-a2be-2dbe2411db21 · outbound

This paper cites and Papaspiliopoulos, O.

Leveraging generative models to assist Monte Carlo sampling and Papaspiliopoulos, O

Reference 43

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Observation ba8b2d1a-0bd9-485f-a4fe-a056aac076b3 · outbound

This paper cites Efficient Modelling of Trivializing Maps for Lattice $\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability.

Leveraging generative models to assist Monte Carlo sampling Efficient Modelling of Trivializing Maps for Lattice $\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability

Reference 44

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Observation 1c588ea1-e69a-4cf7-b0a2-57ceaa0eb00e · outbound

This paper cites Statistics and Computing , volume =.

Leveraging generative models to assist Monte Carlo sampling Statistics and Computing , volume =

Reference 45

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Observation 61c00fe0-46d3-4d36-a82b-541285b1de04 · outbound

This paper cites and Wang, Jian-Sheng , year = 1986, month = nov, journal =.

Leveraging generative models to assist Monte Carlo sampling and Wang, Jian-Sheng , year = 1986, month = nov, journal =

Reference 46

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Observation fdfaa986-fda8-4340-850c-d0f540666dac · outbound

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 47

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Observation 5d2992ac-3234-4746-9b64-3f1ccdce8b0b · outbound

This paper cites Exchange.

Leveraging generative models to assist Monte Carlo sampling Exchange

Reference 48

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Observation a2613bef-3b1e-4c62-b11c-cbf1ca4ea098 · outbound

This paper cites Sequential.

Leveraging generative models to assist Monte Carlo sampling Sequential

Reference 49

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Observation dfbad4bc-da48-47a8-9d0e-e1d4eb0ed2d8 · outbound

This paper cites and Iba, Y.

Leveraging generative models to assist Monte Carlo sampling and Iba, Y

Reference 50

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This paper cites The European Physical Journal C , volume =.

Leveraging generative models to assist Monte Carlo sampling The European Physical Journal C , volume =

Reference 51

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This paper cites Efficient Mapping of Phase Diagrams with Conditional.

Leveraging generative models to assist Monte Carlo sampling Efficient Mapping of Phase Diagrams with Conditional

Reference 52

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Observation d2b1814f-0ec5-456b-b35b-5ef453bb4044 · outbound

This paper cites Machine Learning: Science and Technology , volume =.

Leveraging generative models to assist Monte Carlo sampling Machine Learning: Science and Technology , volume =

Reference 53

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Observation 157509f6-e609-4215-835a-757b9b344f4e · outbound

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 54

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This paper cites and Bose, Joey and Lin, Chen and Klein, Leon and Bronstein, Michael M.

Leveraging generative models to assist Monte Carlo sampling and Bose, Joey and Lin, Chen and Klein, Leon and Bronstein, Michael M

Reference 55

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Observation c4a3118e-816e-42c0-8d21-7928f4830b86 · outbound

This paper cites Stochastic.

Leveraging generative models to assist Monte Carlo sampling Stochastic

Reference 56

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Observation ab06bde5-837a-471d-9746-294445e74ecb · outbound

This paper cites Annealed.

Leveraging generative models to assist Monte Carlo sampling Annealed

Reference 57

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Observation 1538ed5a-bb17-4249-bc4f-1b21f1abe720 · outbound

This paper cites Continual.

Leveraging generative models to assist Monte Carlo sampling Continual

Reference 58

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Observation 094fdb44-f214-4b78-a9be-1df13e6fd142 · outbound

This paper cites Journal of High Energy Physics , volume =.

Leveraging generative models to assist Monte Carlo sampling Journal of High Energy Physics , volume =

Reference 59

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Observation 1fbe2859-b7da-4a2f-a4bc-1920f2b9d03f · outbound

This paper cites , year = 1997, month = apr, journal =.

Leveraging generative models to assist Monte Carlo sampling , year = 1997, month = apr, journal =

Reference 60

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Observation f7cdf7f2-7dba-47a4-a32f-b7e5ec2340a4 · outbound

This paper cites , year = 1998, month = mar, journal =.

Leveraging generative models to assist Monte Carlo sampling , year = 1998, month = mar, journal =

Reference 61

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Observation fc647c9a-f779-47df-ad07-e1a06ac95692 · outbound

This paper cites and Crooks, Gavin E.

Leveraging generative models to assist Monte Carlo sampling and Crooks, Gavin E

Reference 62

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Observation fd6061a7-e838-4708-a7e2-7d77646005d2 · outbound

This paper cites Sampling the Lattice.

Leveraging generative models to assist Monte Carlo sampling Sampling the Lattice

Reference 63

Resolution
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Observation 06027471-63f1-484a-8355-de466958022c · outbound

This paper cites Numerical Determination of the Width and Shape of the Effective String Using.

Leveraging generative models to assist Monte Carlo sampling Numerical Determination of the Width and Shape of the Effective String Using

Reference 64

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Observation a1b1fbf3-7f06-487d-93d9-e6a25f8912fa · outbound

This paper cites Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo.

Leveraging generative models to assist Monte Carlo sampling Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo

Reference 65

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Observation a9a74839-da0b-4068-9e9e-bea2c157e12b · outbound

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Leveraging generative models to assist Monte Carlo sampling Accelerated

Reference 66

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Observation cfb7f63a-bae6-4848-8454-6368c67aeb91 · outbound

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Leveraging generative models to assist Monte Carlo sampling Skipping the

Reference 67

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Observation eb5ff01f-bb05-47c5-9ce5-2cd2ba480c71 · outbound

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Leveraging generative models to assist Monte Carlo sampling Sampling

Reference 68

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Observation 9f4b53e3-6732-4121-b024-35b2c5a384db · outbound

This paper cites Flow Perturbation to Accelerate.

Leveraging generative models to assist Monte Carlo sampling Flow Perturbation to Accelerate

Reference 69

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Observation 37006d3a-504e-47ca-8760-8000f3d0f062 · outbound

This paper cites Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks.

Leveraging generative models to assist Monte Carlo sampling Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

Reference 70

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Observation 25cbe961-5810-4b79-91e7-e7d54ca4704e · outbound

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Leveraging generative models to assist Monte Carlo sampling Enhanced

Reference 71

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Observation 39545cfa-f5dd-46ec-ad9e-0b0e3014be08 · outbound

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Leveraging generative models to assist Monte Carlo sampling Speed-up of

Reference 72

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Observation aca2f4d1-d238-4641-a36d-cf978d3f504b · outbound

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Leveraging generative models to assist Monte Carlo sampling Generalized

Reference 73

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Observation 818830f3-91bc-4a06-ab15-879e3ea696ff · outbound

This paper cites Statistics and Computing , volume =.

Leveraging generative models to assist Monte Carlo sampling Statistics and Computing , volume =

Reference 74

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Observation f4b0c2e7-8f54-4e34-beac-cbdbbf798b09 · outbound

This paper cites Physical Review E , volume =.

Leveraging generative models to assist Monte Carlo sampling Physical Review E , volume =

Reference 75

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Observation 24b362e3-3030-424c-8564-759255df76b7 · outbound

This paper cites and Kucukelbir, Alp and McAuliffe, Jon D.

Leveraging generative models to assist Monte Carlo sampling and Kucukelbir, Alp and McAuliffe, Jon D

Reference 76

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Observation b41993d3-d9a9-424d-b27e-d72f5fd92691 · outbound

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Leveraging generative models to assist Monte Carlo sampling and Ghahramani, Zoubin and Jaakkola, Tommi S

Reference 77

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Observation e8447618-29d7-431a-b18d-caf5e9012de2 · outbound

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Leveraging generative models to assist Monte Carlo sampling Variational

Reference 78

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Observation 675a562b-9743-44bf-9e88-b00ebe5e510b · outbound

This paper cites Solving Statistical Mechanics Using Variational Autoregressive Networks.

Leveraging generative models to assist Monte Carlo sampling Solving Statistical Mechanics Using Variational Autoregressive Networks

Reference 79

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Observation 0536c030-d6ff-498c-9a8e-4bcd2ad1e763 · outbound

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 80

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Observation 9183074d-8633-43fb-b359-1cad85fc367b · outbound

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 81

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Leveraging generative models to assist Monte Carlo sampling Physical Review D , volume =

Reference 82

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Observation 5136d038-e7ba-4783-aabf-ceb7cba8cb1b · outbound

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Leveraging generative models to assist Monte Carlo sampling Forty-Second

Reference 83

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Leveraging generative models to assist Monte Carlo sampling Improving

Reference 84

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This paper cites Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation.

Leveraging generative models to assist Monte Carlo sampling Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation

Reference 85

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 86

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This paper cites Nature Machine Intelligence , volume =.

Leveraging generative models to assist Monte Carlo sampling Nature Machine Intelligence , volume =

Reference 87

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Leveraging generative models to assist Monte Carlo sampling Machine Learning: Science and Technology , volume =

Reference 88

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Leveraging generative models to assist Monte Carlo sampling Markovian

Reference 89

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Leveraging generative models to assist Monte Carlo sampling and Lederman, Roy R

Reference 90

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Leveraging generative models to assist Monte Carlo sampling Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

Reference 91

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Leveraging generative models to assist Monte Carlo sampling Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks

Reference 92

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Leveraging generative models to assist Monte Carlo sampling Machine-Learning-Assisted

Reference 93

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This paper cites Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization.

Leveraging generative models to assist Monte Carlo sampling Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization

Reference 94

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 95

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This paper cites Forty-First.

Leveraging generative models to assist Monte Carlo sampling Forty-First

Reference 96

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 97

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Leveraging generative models to assist Monte Carlo sampling Relaxing

Reference 98

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Leveraging generative models to assist Monte Carlo sampling Theoretical Guarantees for Sampling and Inference in Generative Models with Latent Diffusions , booktitle =

Reference 99

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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 100

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