2022

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, Ling, Zhang, Zhilong, Song, Yang et al.

Understand

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design.

In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures.
We also discuss the potential for combining diffusion models with other generative models for enhanced results.
We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language generation, temporal data modeling, to interdisciplinary applications in other scientific disciplines.

Built on

Generating Long Sequences with Sparse Transformers
Original
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
Earlier work this paper cites.
The monte carlo method
Nicholas Metropolis and Stanislaw Ulam. 1949 · 1949
Earlier work this paper cites.
Correlation functions and computer simulations
Giorgio Parisi. 1981 · 1981
Earlier work this paper cites.
Reverse-time diffusion equation models
Brian DO Anderson. 1982 · 1982
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines
Michael F Hutchinson. 1989 · 1989
Earlier work this paper cites.
A tutorial on hidden Markov models and selected applications in speech recognition
Lawrence R Rabiner. 1989 · 1989
Earlier work this paper cites.
The eigenvalues of mega-dimensional matrices
John Skilling. 1989 · 1989
Earlier work this paper cites.
Representations of knowledge in complex systems
Ulf Grenander and Michael I Miller. 1994 · 1994
Earlier work this paper cites.
Computer methods for ordinary differential equations and differential-algebraic equations . Vol. 61
Uri M Ascher and Linda R Petzold. 1998 · 1998
Earlier work this paper cites.
Taking on the curse of dimensionality in joint distributions using neural networks
Samy Bengio and Yoshua Bengio. 2000 · 2000
Earlier work this paper cites.
The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne. 2000 · 2000
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003 · 2003
Earlier work this paper cites.
A new model for learning in graph domains. In Proceedings. 2005 IEEE international joint conference on neural networks , Vol. 2. 729–734
Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005 · 2005
Earlier work this paper cites.
Estimation of Non-Normalized Statistical Models by Score Matching
Aapo Hyvärinen. 2005 · 2005
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fujie Huang. 2006 · 2006
Earlier work this paper cites.
Learning to be Bayesian without supervision. In Advances in neural information processing systems . 1145–1152
Martin Raphan and Eero P Simoncelli. 2007 · 2007
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008 · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders. In International Conference on Machine Learning . 1096–1103
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008 · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition . 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman. 2009 · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky. 2009 · 2009
Earlier work this paper cites.
On tracking the partition function. In Advances in Neural Information Processing Systems . 2501–2509
Guillaume Desjardins, Yoshua Bengio, and Aaron C Courville. 2011 · 2011
Earlier work this paper cites.
The Neural Autoregressive Distribution Estimator. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS
Hugo Larochelle and Iain Murray. 2011 · 2011
Earlier work this paper cites.
Learning deep energy models. In International Conference on Machine Learning . 1105–1112
Jiquan Ngiam, Zhenghao Chen, Pang W Koh, and Andrew Y Ng. 2011 · 2011
Earlier work this paper cites.
Least squares estimation without priors or supervision
Martin Raphan and Eero P Simoncelli. 2011 · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent. 2011 · 2011
Earlier work this paper cites.
Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012. In 2012 Computing in Cardiology . IEEE, 245–248
Ikaro Silva, George Moody, Daniel J Scott, Leo A Celi, and Roger G Mark. 2012 · 2012
Earlier work this paper cites.
Stochastic optimization
James C Spall. 2012 · 2012
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Original
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
Earlier work this paper cites.
Generating Sequences With Recurrent Neural Networks
Original
Alex Graves. 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Original
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Characterization and computation of local Nash equilibria in continuous games. In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 917–924
Lillian J Ratliff, Samuel A Burden, and S Shankar Sastry. 2013 · 2013
Earlier work this paper cites.
High-dimensional probability estimation with deep density models
Original
Oren Rippel and Ryan Prescott Adams. 2013 · 2013
Earlier work this paper cites.
A tensor-based method for missing traffic data completion
Huachun Tan, Guangdong Feng, Jianshuai Feng, Wuhong Wang, Yu-Jin Zhang, and Feng Li. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets. In Advances in Neural Information Processing Systems , Vol. 27. 139–144
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models. In International Conference on Machine Learning . 1278–1286
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints. In Advances in Neural Information Processing Systems , Vol. 28
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams. 2015 · 2015
Earlier work this paper cites.
Variational inference with normalizing flows. In International Conference on Machine Learning . 1530–1538
Danilo Rezende and Shakir Mohamed. 2015 · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning . 2256–2265
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In International Conference on Machine Learning , Francis R. Bach and David M. Blei (Eds.). 2256–2265
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
Original
Lucas Theis, Aäron van den Oord, and Matthias Bethge. 2015 · 2015
Earlier work this paper cites.
Density estimation using real nvp
Original
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
Tutorial on variational autoencoders
Original
Carl Doersch. 2016 · 2016
Earlier work this paper cites.
Testing the manifold hypothesis
Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan. 2016 · 2016
Earlier work this paper cites.
A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models
Original
Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine. 2016 · 2016
Earlier work this paper cites.
Deep directed generative models with energy-based probability estimation
Original
Taesup Kim and Yoshua Bengio. 2016 · 2016
Earlier work this paper cites.
A theory of generative convnet. In International Conference on Machine Learning . 2635–2644
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu. 2016 · 2016
Earlier work this paper cites.
ST-MVL: filling missing values in geo-sensory time series data. In Proceedings of the 25th International Joint Conference on Artificial Intelligence
Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li. 2016 · 2016
Earlier work this paper cites.
Energy-based generative adversarial network
Original
Junbo Zhao, Michael Mathieu, and Yann LeCun. 2016 · 2016
Earlier work this paper cites.
Density estimation using Real NVP. In International Conference on Learning Representations
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2017 · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry. In International Conference on Machine Learning . 1263–1272
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Variational walkback: Learning a transition operator as a stochastic recurrent net. In Advances in Neural Information Processing Systems . 4392–4402
Anirudh Goyal Alias Parth Goyal, Nan Rosemary Ke, Surya Ganguli, and Yoshua Bengio. 2017 · 2017
Earlier work this paper cites.
Representation learning on graphs: Methods and applications
Original
William L Hamilton, Rex Ying, and Jure Leskovec. 2017b · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks. In IEEE Conference on Computer Vision and Pattern Recognition . 1125–1134
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017 · 2017
Earlier work this paper cites.
Introspective classification with convolutional nets. In Advances in Neural Information Processing Systems , Vol. 30. 823–833
Long Jin, Justin Lazarow, and Zhuowen Tu. 2017 · 2017
Earlier work this paper cites.
Introspective neural networks for generative modeling. In Proceedings of the IEEE International Conference on Computer Vision . 2774–2783
Justin Lazarow, Long Jin, and Zhuowen Tu. 2017 · 2017
Earlier work this paper cites.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging . Springer, 146–157
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017 · 2017
Earlier work this paper cites.
Demystifying MMD GANs. In International Conference on Learning Representations
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton. 2018 · 2018
Earlier work this paper cites.
Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh. 2018 · 2018
Earlier work this paper cites.
Brits: Bidirectional recurrent imputation for time series. In Advances in Neural Information Processing Systems , Vol. 31
Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. 2018 · 2018
Earlier work this paper cites.
Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018 · 2018
Earlier work this paper cites.
Neural ordinary differential equations
Original
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018 · 2018
Earlier work this paper cites.
Generative adversarial networks: An overview
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath. 2018 · 2018
Earlier work this paper cites.
Learning generative convnets via multi-grid modeling and sampling. In IEEE Conference on Computer Vision and Pattern Recognition . 9155–9164
Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, and Ying Nian Wu. 2018 · 2018
Earlier work this paper cites.
World models
Original
David Ha and Jürgen Schmidhuber. 2018 · 2018
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation. In International Conference on Machine Learning . 2323–2332
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018 · 2018
Earlier work this paper cites.
Image generation from scene graphs. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1219–1228
Justin Johnson, Agrim Gupta, and Li Fei-Fei. 2018 · 2018
Earlier work this paper cites.
Efficient Neural Audio Synthesis. In International Conference on Machine Learning . 2410–2419
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, and Koray Kavukcuoglu. 2018 · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Original
Diederik P Kingma and Prafulla Dhariwal. 2018 · 2018
Earlier work this paper cites.
Wasserstein introspective neural networks. In IEEE Conference on Computer Vision and Pattern Recognition . 3702–3711
Kwonjoon Lee, Weijian Xu, Fan Fan, and Zhuowen Tu. 2018 · 2018
Earlier work this paper cites.
Multivariate time series imputation with generative adversarial networks. In Advances in Neural Information Processing Systems , Vol. 31
Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al · 2018
Earlier work this paper cites.
Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Earlier work this paper cites.
On the State of the Art of Evaluation in Neural Language Models. In International Conference on Learning Representations
Gábor Melis, Chris Dyer, and Phil Blunsom. 2018 · 2018
Earlier work this paper cites.
Regularizing and Optimizing LSTM Language Models. In International Conference on Learning Representations
Stephen Merity, Nitish Shirish Keskar, and Richard Socher. 2018 · 2018
Earlier work this paper cites.
Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation. In International Conference on Learning Representations
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 2019
Earlier work this paper cites.
A RAD approach to deep mixture models
Original
Laurent Dinh, Jascha Sohl-Dickstein, Hugo Larochelle, and Razvan Pascanu. 2019 · 2019
Earlier work this paper cites.
Implicit generation and generalization in energy-based models
Original
Yilun Du and Igor Mordatch. 2019 · 2019
Earlier work this paper cites.
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Original
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. 2019b · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 4401–4410
Tero Karras, Samuli Laine, and Timo Aila. 2019 · 2019
Earlier work this paper cites.
Ctrl: A conditional transformer language model for controllable generation
Original
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 2019
Earlier work this paper cites.
An introduction to variational autoencoders
Diederik P Kingma, Max Welling, et al · 2019
Earlier work this paper cites.
Maximum Entropy Generators for Energy-Based Models
Original
Rithesh Kumar, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. 2019 · 2019
Earlier work this paper cites.
Pastegan: A semi-parametric method to generate image from scene graph
Yikang Li, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, and Xiaogang Wang. 2019b · 2019
Earlier work this paper cites.
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Original
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu. 2019a · 2019
Earlier work this paper cites.
On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model
Original
Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. 2019b · 2019
Earlier work this paper cites.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2019 · 2019
Earlier work this paper cites.
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models. In International Conference on Learning Representations
Yixuan Qiu, Lingsong Zhang, and Xiao Wang. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
High-dimensional multivariate forecasting with low-rank gaussian copula processes. In Advances in Neural Information Processing Systems , Vol. 32
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus. 2019 · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems , Vol. 32
Yang Song and Stefano Ermon. 2019 · 2019
Earlier work this paper cites.
Sliced Score Matching: A Scalable Approach to Density and Score Estimation. In Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019 . 204
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon. 2019 · 2019
Earlier work this paper cites.
Time-series generative adversarial networks. In Advances in Neural Information Processing Systems , Vol. 32
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019 · 2019
Earlier work this paper cites.
PyOD: A Python Toolbox for Scalable Outlier Detection
Yue Zhao, Zain Nasrullah, and Zheng Li. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners. In Advances in Neural Information Processing Systems
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Learning gradient fields for shape generation. In European Conference on Computer Vision . Springer, 364–381
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan. 2020 · 2020
Earlier work this paper cites.
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Original
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio. 2020 · 2020
Earlier work this paper cites.
WaveGrad: Estimating gradients for waveform generation
Original
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. 2020 · 2020
Earlier work this paper cites.
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images. In International Conference on Learning Representations
Rewon Child. 2020 · 2020
Earlier work this paper cites.
Relaxing bijectivity constraints with continuously indexed normalising flows. In International Conference on Machine Learning . 2133–2143
Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet. 2020 · 2020
Earlier work this paper cites.
Gp-vae: Deep probabilistic time series imputation. In International conference on artificial intelligence and statistics . PMLR, 1651–1661
Vincent Fortuin, Dmitry Baranchuk, Gunnar Ratsch, and Stephan Mandt. 2020 · 2020
Earlier work this paper cites.
Learning energy-based models by diffusion recovery likelihood
Original
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma. 2020b · 2020
Earlier work this paper cites.
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling
Original
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, and Richard Zemel. 2020 · 2020
Earlier work this paper cites.
Learning canonical representations for scene graph to image generation. In European Conference on Computer Vision . 210–227
Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, and Amir Globerson. 2020 · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems , Vol. 33. 6840–6851
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Earlier work this paper cites.
The survey: Text generation models in deep learning
Touseef Iqbal and Shaima Qureshi. 2020 · 2020
Earlier work this paper cites.
Diffwave: A versatile diffusion model for audio synthesis
Original
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2020 · 2020
Earlier work this paper cites.
Gedi: Generative discriminator guided sequence generation
Original
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2020 · 2020
Earlier work this paper cites.
Neural manifold ordinary differential equations
Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser Nam Lim, and Christopher M De Sa. 2020 · 2020
Earlier work this paper cites.
Riemannian continuous normalizing flows
Emile Mathieu and Maximilian Nickel. 2020 · 2020
Earlier work this paper cites.
Autoregressive score matching
Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, and Stefano Ermon. 2020b · 2020
Earlier work this paper cites.
Permutation invariant graph generation via score-based generative modeling. In International Conference on Artificial Intelligence and Statistics . PMLR, 4474–4484
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. 2020 · 2020
Earlier work this paper cites.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. In International Conference on Learning Representations
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. 2020 · 2020
Earlier work this paper cites.
Adversarial latent autoencoders. In IEEE Conference on Computer Vision and Pattern Recognition . 14104–14113
Stanislav Pidhorskyi, Donald A Adjeroh, and Gianfranco Doretto. 2020 · 2020
Earlier work this paper cites.
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. In International Conference on Learning Representations
Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M Bergmann, and Roland Vollgraf. 2020 · 2020
Earlier work this paper cites.

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Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022 · 2022
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Understanding Diffusion Models: A Unified Perspective
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Antigen-specific antibody design and optimization with diffusion-based generative models
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma. 2022 · 2022
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Original
Zhaoyang Lyu, Xudong Xu, Ceyuan Yang, Dahua Lin, and Bo Dai. 2022 · 2022
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Siyuan Mei, Fuxin Fan, and Andreas Maier. 2022 · 2022
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In International Conference on Machine Learning . 16784–16804
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022 · 2022
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Diffusion Models for Adversarial Purification
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Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Muzaffer Özbey, Salman UH Dar, Hasan A Bedel, Onat Dalmaz, Şaban Özturk, Alper Güngör, and Tolga Çukur. 2022 · 2022
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Scalable Diffusion Models with Transformers
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William Peebles and Saining Xie. 2022 · 2022
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Towards performant and reliable undersampled MR reconstruction via diffusion model sampling. In International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 623–633
Cheng Peng, Pengfei Guo, S Kevin Zhou, Vishal M Patel, and Rama Chellappa. 2022 · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Original
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. 2022 · 2022
Closest in time.
Diffusion autoencoders: Toward a meaningful and decodable representation. In IEEE Conference on Computer Vision and Pattern Recognition . 10619–10629
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn. 2022 · 2022
Closest in time.
Hierarchical text-conditional image generation with clip latents
Original
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Closest in time.
High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Closest in time.
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Original
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2022 · 2022
Closest in time.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Original
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
Closest in time.
Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. 2022c · 2022
Closest in time.
Conditional simulation using diffusion Schrödinger bridges
Original
Yuyang Shi, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet. 2022 · 2022
Closest in time.
Make-a-video: Text-to-video generation without text-video data
Original
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al · 2022
Closest in time.
Applying Regularized Schrödinger-Bridge-Based Stochastic Process in Generative Modeling
Original
Ki-Ung Song. 2022 · 2022
Closest in time.
PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition
Original
Jiachen Sun, Weili Nie, Zhiding Yu, Z Morley Mao, and Chaowei Xiao. 2022 · 2022
Closest in time.
Human motion diffusion model
Original
Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. 2022 · 2022
Closest in time.
UniTune: Text-Driven Image Editing by Fine Tuning an Image Generation Model on a Single Image
Original
Dani Valevski, Matan Kalman, Yossi Matias, and Yaniv Leviathan. 2022 · 2022
Closest in time.
Guided Diffusion Model for Adversarial Purification
Original
Jinyi Wang, Zhaoyang Lyu, Dahua Lin, Bo Dai, and Hongfei Fu. 2022a · 2022
Closest in time.
KnowDA: All-in-one knowledge mixture model for data augmentation in few-shot nlp
Original
Yufei Wang, Jiayi Zheng, Can Xu, Xiubo Geng, Tao Shen, Chongyang Tao, and Daxin Jiang. 2022c · 2022
Closest in time.
Diffusion-GAN: Training GANs with Diffusion
Original
Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022b · 2022
Closest in time.
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Closest in time.
Deblurring via stochastic refinement. In IEEE Conference on Computer Vision and Pattern Recognition . 16293–16303
Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G Dimakis, and Peyman Milanfar. 2022 · 2022
Closest in time.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
Original
Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Weixian Lei, Yuchao Gu, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. 2022a · 2022
Closest in time.
Guided Diffusion Model for Adversarial Purification from Random Noise
Original
Quanlin Wu, Hang Ye, and Yuntian Gu. 2022b · 2022
Closest in time.
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise. In IEEE Conference on Computer Vision and Pattern Recognition . 650–656
Julian Wyatt, Adam Leach, Sebastian M Schmon, and Chris G Willcocks. 2022 · 2022
Closest in time.
Vector Quantized Diffusion Model with CodeUnet for Text-to-Sign Pose Sequences Generation
Original
Pan Xie, Qipeng Zhang, Zexian Li, Hao Tang, Yao Du, and Xiaohui Hu. 2022 · 2022
Closest in time.
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction
Original
Yutong Xie and Quanzheng Li. 2022 · 2022
Closest in time.
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
Original
Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. 2022a · 2022
Closest in time.
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
Original
Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, and Humphrey Shi. 2022b · 2022
Closest in time.
Diffsound: Discrete Diffusion Model for Text-to-sound Generation
Original
Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, and Dong Yu. 2022c · 2022
Closest in time.
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Original
Ling Yang and Shenda Hong. 2022a · 2022
Closest in time.
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Original
Ling Yang, Zhilin Huang, Yang Song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, and Ming-Hsuan Yang. 2022a · 2022
Closest in time.
Lossy Image Compression with Conditional Diffusion Models
Original
Ruihan Yang and Stephan Mandt. 2022 · 2022
Closest in time.
Diffusion probabilistic modeling for video generation
Original
Ruihan Yang, Prakhar Srivastava, and Stephan Mandt. 2022b · 2022
Closest in time.
Coca: Contrastive captioners are image-text foundation models
Original
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. 2022b · 2022
Closest in time.
Generating videos with dynamics-aware implicit generative adversarial networks
Original
Sihyun Yu, Jihoon Tack, Sangwoo Mo, Hyunsu Kim, Junho Kim, Jung-Woo Ha, and Jinwoo Shin. 2022a · 2022
Closest in time.
LION: Latent Point Diffusion Models for 3D Shape Generation. In Advances in Neural Information Processing Systems
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. 2022 · 2022
Closest in time.
Motiondiffuse: Text-driven human motion generation with diffusion model
Original
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. 2022a · 2022
Closest in time.
Fast Sampling of Diffusion Models with Exponential Integrator
Original
Qinsheng Zhang and Yongxin Chen. 2022 · 2022
Closest in time.
gDDIM: Generalized denoising diffusion implicit models
Original
Qinsheng Zhang, Molei Tao, and Yongxin Chen. 2022c · 2022
Closest in time.
Opt: Open pre-trained transformer language models
Original
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
Closest in time.
Cross Reconstruction Transformer for Self-Supervised Time Series Representation Learning
Original
Wenrui Zhang, Ling Yang, Shijia Geng, and Shenda Hong. 2022d · 2022
Closest in time.
Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
Original
Min Zhao, Fan Bao, Chongxuan Li, and Jun Zhu. 2022 · 2022
Closest in time.
Truncated diffusion probabilistic models
Original
Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022 · 2022
Closest in time.
Discrete contrastive diffusion for cross-modal and conditional generation
Original
Ye Zhu, Yu Wu, Kyle Olszewski, Jian Ren, Sergey Tulyakov, and Yan Yan. 2022 · 2022
Closest in time.
Gpt-4 technical report
Original
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Closest in time.
Palm 2 technical report
Original
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Closest in time.
Leaving Reality to Imagination: Robust Classification via Generated Datasets. In International Conference on Learning Representations
Hritik Bansal and Aditya Grover. 2023 · 2023
Closest in time.
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Original
Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, and Jun Zhu. 2023 · 2023
Closest in time.
Improving image generation with better captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 2023
Closest in time.
Instructpix2pix: Learning to follow image editing instructions. In IEEE Conference on Computer Vision and Pattern Recognition . 18392–18402
Tim Brooks, Aleksander Holynski, and Alexei A Efros. 2023 · 2023
Closest in time.
Sequence to sequence text generation with diffusion models. In International Conference on Learning Representations
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2023 · 2023
Closest in time.
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction. In International Conference on Learning Representations
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 2023 · 2023
Closest in time.
Shap-e: Generating conditional 3d implicit functions
Original
Heewoo Jun and Alex Nichol. 2023 · 2023
Closest in time.
Text2video-zero: Text-to-image diffusion models are zero-shot video generators
Original
Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. 2023 · 2023
Closest in time.
Aligning text-to-image models using human feedback
Original
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu. 2023 · 2023
Closest in time.
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
Original
Long Lian, Boyi Li, Adam Yala, and Trevor Darrell. 2023 · 2023
Closest in time.
One transformer can understand both 2d & 3d molecular data. In International Conference on Learning Representations
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He. 2023 · 2023
Closest in time.
GPT-4 Technical Report
Original
OpenAI. 2023a · 2023
Closest in time.
Gpt-4 technical report. arxiv 2303.08774
R OpenAI. 2023b · 2023
Closest in time.
Sdxl: Improving latent diffusion models for high-resolution image synthesis
Original
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023 · 2023
Closest in time.
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing
Original
Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, and Qifeng Chen. 2023 · 2023
Closest in time.
3d neural field generation using triplane diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 20875–20886
J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein. 2023 · 2023
Closest in time.
Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior
Original
Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Original
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
Effective Data Augmentation With Diffusion Models. In International Conference on Learning Representations
Brandon Trabucco, Kyle Doherty, Max Gurinas, and Ruslan Salakhutdinov. 2023 · 2023
Closest in time.
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. In International Conference on Learning Representations
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola. 2023 · 2023
Closest in time.
Cg3d: Compositional generation for text-to-3d via gaussian splatting
Original
Alexander Vilesov, Pradyumna Chari, and Achuta Kadambi. 2023 · 2023
Closest in time.
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
Original
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. 2023 · 2023
Closest in time.
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
Original
Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan. 2023 · 2023
Closest in time.
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models. In IEEE Conference on Computer Vision and Pattern Recognition
Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. 2023 · 2023
Closest in time.
Score-Based Graph Generative Modeling with Self-Guided Latent Diffusion
Ling Yang, Zhilong Zhang, Wentao Zhang, and Shenda Hong. 2023b · 2023
Closest in time.
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
Original
Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, and Jun Zhu. 2023 · 2023
Closest in time.
Pre-training via denoising for molecular property prediction. In International Conference on Learning Representations
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin. 2023 · 2023
Closest in time.
Ipdreamer: Appearance-controllable 3d object generation with image prompts
Original
Bohan Zeng, Shanglin Li, Yutang Feng, Ling Yang, Hong Li, Sicheng Gao, Jiaming Liu, Conghui He, Wentao Zhang, Jianzhuang Liu, Baochang Zhang, and Shuicheng Yan. 2023 · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Original
Lvmin Zhang and Maneesh Agrawala. 2023 · 2023
Closest in time.
Multimodal chain-of-thought reasoning in language models
Original
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. 2023 · 2023
Closest in time.
Uni-mol: A universal 3d molecular representation learning framework. In International Conference on Learning Representations
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. 2023 · 2023
Closest in time.
Comboverse: Compositional 3d assets creation using spatially-aware diffusion guidance
Original
Yongwei Chen, Tengfei Wang, Tong Wu, Xingang Pan, Kui Jia, and Ziwei Liu. 2024 · 2024
Closest in time.
Disentangled 3D Scene Generation with Layout Learning. In International Conference on Machine Learning
Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A Efros, and Aleksander Holynski. 2024 · 2024
Closest in time.
Reinforcement learning for fine-tuning text-to-image diffusion models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee. 2024 · 2024
Closest in time.
Graphdreamer: Compositional 3d scene synthesis from scene graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 21295–21304
Gege Gao, Weiyang Liu, Anpei Chen, Andreas Geiger, and Bernhard Schölkopf. 2024 · 2024
Closest in time.
REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment
Original
Haonan Han, Rui Yang, Huan Liao, Jiankai Xing, Zunnan Xu, Xiaoming Yu, Junwei Zha, Xiu Li, and Wanhua Li. 2024 · 2024
Closest in time.
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
Original
Roberto Henschel, Levon Khachatryan, Daniil Hayrapetyan, Hayk Poghosyan, Vahram Tadevosyan, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. 2024 · 2024
Closest in time.
Mixtral of experts
Original
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
Closest in time.
Retrieval-Augmented Diffusion Models for Time Series Forecasting. In Advances in Neural Information Processing Systems
Jingwei Liu, Ling Yang, Hongyan Li, and Shenda Hong. 2024 · 2024
Closest in time.
MVDream: Multi-view Diffusion for 3D Generation. In The Twelfth International Conference on Learning Representations
Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. 2024 · 2024
Closest in time.
VideoTetris: Towards Compositional Text-to-Video Generation
Ye Tian, Ling Yang, Haotian Yang, Yuan Gao, Yufan Deng, Jingmin Chen, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, Di Zhang, and Bin Cui. 2024 · 2024
Closest in time.
Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 8228–8238
Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. 2024 · 2024
Closest in time.
Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
Original
Fu-Yun Wang, Ling Yang, Zhaoyang Huang, Mengdi Wang, and Hongsheng Li. 2024 · 2024
Closest in time.
Imagereward: Learning and evaluating human preferences for text-to-image generation
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. 2024 · 2024
Closest in time.
Graphusion: Latent Diffusion for Graph Generation
Ling Yang, Zhilin Huang, Zhilong Zhang, Zhongyi Liu, Shenda Hong, Wentao Zhang, Wenming Yang, Bin Cui, and Luxia Zhang. 2024a · 2024
Closest in time.
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Original
Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, and Bin Cui. 2024e · 2024
Closest in time.
EditWorld: Simulating World Dynamics for Instruction-Following Image Editing
Original
Ling Yang, Bohan Zeng, Jiaming Liu, Hong Li, Minghao Xu, Wentao Zhang, and Shuicheng Yan. 2024f · 2024
Closest in time.
Semantic Score Distillation Sampling for Compositional Text-to-3D Generation
Original
Ling Yang, Zixiang Zhang, Junlin Han, Bohan Zeng, Runjia Li, Philip Torr, and Wentao Zhang. 2024g · 2024
Closest in time.
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
Original
Ling Yang, Zixiang Zhang, Zhilong Zhang, Xingchao Liu, Minkai Xu, Wentao Zhang, Chenlin Meng, Stefano Ermon, and Bin Cui. 2024i · 2024
Closest in time.
Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis
Original
Bohan Zeng, Ling Yang, Siyu Li, Jiaming Liu, Zixiang Zhang, Juanxi Tian, Kaixin Zhu, Yongzhen Guo, Fu-Yun Wang, Minkai Xu, Stefano Ermon, and Wentao Zhang. 2024 · 2024
Closest in time.
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
Original
Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Kaini Wang, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, and Bin Cui. 2024a · 2024
Closest in time.
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
Original
Xinchen Zhang, Ling Yang, Guohao Li, Yaqi Cai, Jiake Xie, Yong Tang, Yujiu Yang, Mengdi Wang, and Bin Cui. 2024b · 2024
Closest in time.
Distribution-Aware Data Expansion with Diffusion Models
Original
Haowei Zhu, Ling Yang, Jun-Hai Yong, Wentao Zhang, and Bin Wang. 2024 · 2024
Closest in time.
Vlogger: Make Your Dream A Vlog
Original
Shaobin Zhuang, Kunchang Li, Xinyuan Chen, Yaohui Wang, Ziwei Liu, Yu Qiao, and Yali Wang. 2024 · 2024
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Original
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
Closest in time.
Preacher: Paper-to-Video Agentic System
Original
Jingwei Liu, Ling Yang, Hao Luo, Fan Wang Hongyan Li, and Mengdi Wang. 2025 · 2025
Closest in time.
Revolutionizing reinforcement learning framework for diffusion large language models
Original
Yinjie Wang, Ling Yang, Bowen Li, Ye Tian, Ke Shen, and Mengdi Wang. 2025a · 2025
Closest in time.
Co-evolving llm coder and unit tester via reinforcement learning
Original
Yinjie Wang, Ling Yang, Ye Tian, Ke Shen, and Mengdi Wang. 2025b · 2025
Closest in time.
Mmada: Multimodal large diffusion language models
Original
Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang. 2025a · 2025
Closest in time.
Reasonflux: Hierarchical llm reasoning via scaling thought templates
Original
Ling Yang, Zhaochen Yu, Bin Cui, and Mengdi Wang. 2025b · 2025
Closest in time.
ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
Original
Jiaru Zou, Ling Yang, Jingwen Gu, Jiahao Qiu, Ke Shen, Jingrui He, and Mengdi Wang. 2025 · 2025
Closest in time.

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