ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2310.02679
  4. Cited By
Diffusion Generative Flow Samplers: Improving learning signals through
  partial trajectory optimization
v1v2v3 (latest)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization

4 October 2023
Dinghuai Zhang
Ricky Tian Qi Chen
Cheng-Hao Liu
Aaron C. Courville
Yoshua Bengio
ArXiv (abs)PDFHTML

Papers citing "Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization"

38 / 38 papers shown
Title
Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards
Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards
Qingming Liu
Zhen Liu
Dinghuai Zhang
Kui Jia
48
0
0
18 Jun 2025
Adaptive Destruction Processes for Diffusion Samplers
Adaptive Destruction Processes for Diffusion Samplers
Timofei Gritsaev
Nikita Morozov
Kirill Tamogashev
D. Tiapkin
S. Samsonov
A. Naumov
Dmitry Vetrov
Nikolay Malkin
64
0
0
02 Jun 2025
On scalable and efficient training of diffusion samplers
On scalable and efficient training of diffusion samplers
Minkyu Kim
Kiyoung Seong
Dongyeop Woo
SungSoo Ahn
Minsu Kim
DiffM
157
0
0
26 May 2025
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Chenguang Wang
Xiaoyu Zhang
Kaiyuan Cui
Weichen Zhao
Yongtao Guan
Tianshu Yu
DiffM
115
0
0
26 May 2025
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion
Abrar Rahman Abir
Haz Sameen Shahgir
Md Rownok Zahan Ratul
Md Toki Tahmid
Greg Ver Steeg
Yue Dong
97
0
0
18 May 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron J. Havens
Benjamin Kurt Miller
Bing Yan
Carles Domingo-Enrich
Anuroop Sriram
...
Brandon Amos
Brian Karrer
Xiang Fu
Guan-Horng Liu
Ricky T. Q. Chen
DiffM
186
3
0
16 Apr 2025
Underdamped Diffusion Bridges with Applications to Sampling
Denis Blessing
Julius Berner
Lorenz Richter
Gerhard Neumann
DiffM
142
6
0
02 Mar 2025
End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler
Denis Blessing
Xiaogang Jia
Gerhard Neumann
DiffM
105
1
0
01 Mar 2025
Single-Step Consistent Diffusion Samplers
Single-Step Consistent Diffusion Samplers
Pascal Jutras-Dubé
Patrick Pynadath
Ruqi Zhang
DiffM
172
0
0
17 Feb 2025
Neural Flow Samplers with Shortcut Models
Neural Flow Samplers with Shortcut Models
Wuhao Chen
Zijing Ou
Yingzhen Li
259
2
0
11 Feb 2025
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
Julius Berner
Lorenz Richter
Marcin Sendera
Jarrid Rector-Brooks
Nikolay Malkin
OffRL
178
8
0
10 Jan 2025
Sequential Controlled Langevin Diffusions
Sequential Controlled Langevin Diffusions
Junhua Chen
Lorenz Richter
Julius Berner
Denis Blessing
Gerhard Neumann
A. Anandkumar
140
22
0
10 Dec 2024
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu
Tim Z. Xiao
Weiyang Liu
Yoshua Bengio
Dinghuai Zhang
264
6
0
10 Dec 2024
Training Neural Samplers with Reverse Diffusive KL Divergence
Training Neural Samplers with Reverse Diffusive KL Divergence
Wenlin Chen
Jiajun He
Mingtian Zhang
David Barber
José Miguel Hernández-Lobato
DiffM
141
8
0
16 Oct 2024
On Divergence Measures for Training GFlowNets
On Divergence Measures for Training GFlowNets
Tiago da Silva
Eliezer de Souza da Silva
Diego Mesquita
BDL
161
3
0
12 Oct 2024
Beyond Squared Error: Exploring Loss Design for Enhanced Training of
  Generative Flow Networks
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
Rui Hu
Yifan Zhang
Zhuoran Li
Longbo Huang
101
2
0
03 Oct 2024
Adaptive teachers for amortized samplers
Adaptive teachers for amortized samplers
Minsu Kim
Sanghyeok Choi
Taeyoung Yun
Emmanuel Bengio
Leo Feng
Jarrid Rector-Brooks
Sungsoo Ahn
Jinkyoo Park
Nikolay Malkin
Yoshua Bengio
490
7
0
02 Oct 2024
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic
  process by leveraging Hamilton-Jacobi PDEs and score-based generative models
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models
Tingwei Meng
Zongren Zou
Jérome Darbon
George Karniadakis
DiffM
88
2
0
15 Sep 2024
Dynamical Measure Transport and Neural PDE Solvers for Sampling
Dynamical Measure Transport and Neural PDE Solvers for Sampling
Jingtong Sun
Julius Berner
Lorenz Richter
Marius Zeinhofer
Johannes Müller
Kamyar Azizzadenesheli
Anima Anandkumar
OTDiffM
97
11
0
10 Jul 2024
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for
  Sampling
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
Denis Blessing
Xiaogang Jia
Johannes Esslinger
Francisco Vargas
Gerhard Neumann
161
27
0
11 Jun 2024
Baking Symmetry into GFlowNets
Baking Symmetry into GFlowNets
George Ma
Emmanuel Bengio
Yoshua Bengio
Dinghuai Zhang
113
12
0
08 Jun 2024
Improving GFlowNets for Text-to-Image Diffusion Alignment
Improving GFlowNets for Text-to-Image Diffusion Alignment
Dinghuai Zhang
Yizhe Zhang
Jiatao Gu
Ruixiang Zhang
J. Susskind
Navdeep Jaitly
Shuangfei Zhai
EGVM
140
10
0
02 Jun 2024
Amortizing intractable inference in diffusion models for vision, language, and control
Amortizing intractable inference in diffusion models for vision, language, and control
S. Venkatraman
Moksh Jain
Luca Scimeca
Minsu Kim
Marcin Sendera
...
Alexandre Adam
Jarrid Rector-Brooks
Yoshua Bengio
Glen Berseth
Nikolay Malkin
191
32
0
31 May 2024
Pessimistic Backward Policy for GFlowNets
Pessimistic Backward Policy for GFlowNets
Hyosoon Jang
Yunhui Jang
Minsu Kim
Jinkyoo Park
SungSoo Ahn
121
7
0
25 May 2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing
  Flows
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
A. Cabezas
Louis Sharrock
Christopher Nemeth
74
4
0
23 May 2024
Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Shuai Wang
David W. Zhang
Jia-Hong Huang
Stevan Rudinac
Monika Kackovic
Nachoem Wijnberg
Marcel Worring
69
1
0
22 May 2024
Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with
  Reward-Dependent Adjustment Mechanisms
Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanisms
Shuai Guo
Jielei Chu
Lei Zhu
Zhaoyu Li
Tianrui Li
AI4CE
103
2
0
08 Apr 2024
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized
  Control
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
Masatoshi Uehara
Yulai Zhao
Kevin Black
Ehsan Hajiramezanali
Gabriele Scalia
N. Diamant
Alex Tseng
Tommaso Biancalani
Sergey Levine
94
52
0
23 Feb 2024
Target Score Matching
Target Score Matching
Valentin De Bortoli
M. Hutchinson
Peter Wirnsberger
Arnaud Doucet
DiffM
94
21
0
13 Feb 2024
Particle Denoising Diffusion Sampler
Particle Denoising Diffusion Sampler
Angus Phillips
Hai-Dang Dau
M. Hutchinson
Valentin De Bortoli
George Deligiannidis
Arnaud Doucet
DiffM
138
30
0
09 Feb 2024
Improved off-policy training of diffusion samplers
Improved off-policy training of diffusion samplers
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
OffRL
239
28
0
07 Feb 2024
Sampling in Unit Time with Kernel Fisher-Rao Flow
Sampling in Unit Time with Kernel Fisher-Rao Flow
A. Maurais
Youssef Marzouk
72
18
0
08 Jan 2024
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
Wasu Top Piriyakulkij
Yingheng Wang
Volodymyr Kuleshov
DiffM
145
1
0
05 Jan 2024
Learning to Scale Logits for Temperature-Conditional GFlowNets
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim
Joohwan Ko
Taeyoung Yun
Dinghuai Zhang
Ling Pan
W. Kim
Jinkyoo Park
Emmanuel Bengio
Yoshua Bengio
AI4CE
119
25
0
04 Oct 2023
SE(3) Equivariant Augmented Coupling Flows
SE(3) Equivariant Augmented Coupling Flows
Laurence I. Midgley
Vincent Stimper
Javier Antorán
Emile Mathieu
Bernhard Schölkopf
José Miguel Hernández-Lobato
192
27
0
20 Aug 2023
Reverse Diffusion Monte Carlo
Reverse Diffusion Monte Carlo
Xunpeng Huang
Hanze Dong
Yi Hao
Yi-An Ma
Tong Zhang
DiffM
120
28
0
05 Jul 2023
Improved sampling via learned diffusions
Improved sampling via learned diffusions
Lorenz Richter
Julius Berner
DiffM
121
65
0
03 Jul 2023
Distributional GFlowNets with Quantile Flows
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang
L. Pan
Ricky T. Q. Chen
Aaron Courville
Yoshua Bengio
96
28
0
11 Feb 2023
1