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Flow matching has emerged as a powerful framework for generative modeling, offering computational advantages over diffusion models by leveraging deterministic Ordinary Differential Equations (ODEs) instead of stochastic dynamics.
Solving ordinary differential equations using taylor series
George Corliss and YF Chang · 1982
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Numerical Solution of Stochastic Differential Equations
Peter E Kloeden and Eckhard Platen · 1992
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Atomft: solving odes and daes using taylor series
YF Chang and George Corliss · 1994
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Scalable reversible generative models with free-form continuous dynamics
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, and David Duvenaud · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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How to train your neural ode: the world of jacobian and kinetic regularization
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, and Adam Oberman · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Learning differential equations that are easy to solve
Jacob Kelly, Jesse Bettencourt, Matthew J Johnson, and David K Duvenaud · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Gur AriGuy, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Attentive walk-aggregating graph neural networks
Mehmet F Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, and Yingyu Liang · 2022
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Taylor-lagrange neural ordinary differential equations: Toward fast training and evaluation of neural odes
Franck Djeumou, Cyrus Neary, Eric Goubault, Sylvie Putot, and Ufuk Topcu · 2022
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A nearly optimal size coreset algorithm with nearly linear time
Yichuan Deng, Zhao Song, Yitan Wang, and Yuanyuan Yang · 2022
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Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 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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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Black-box tuning for language-model-as-a-service
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, and Xipeng Qiu · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Denoising time cycle modeling for recommendation
Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen, and Wenliang Zhong · 2022
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Extracting trigger-sharing events via an event matrix
Jun Xu, Weidi Xu, Mengshu Sun, Taifeng Wang, and Wei Chu · 2022
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Gmflow: Learning optical flow via global matching
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao · 2022
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Building normalizing flows with stochastic interpolants
Michael Samuel Albergo and Eric Vanden-Eijnden · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
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All are worth words: A vit backbone for diffusion models
Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu · 2023
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Flow matching for scalable simulation-based inference
Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R Green, Jakob H Macke, and Bernhard Scholkopf · 2023
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Llama-adapter v2: Parameter-efficient visual instruction model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, et al · 2023
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An over-parameterized exponential regression
Yeqi Gao, Sridhar Mahadevan, and Zhao Song · 2023
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Yeqi Gao, Zhao Song, Weixin Wang, and Junze Yin · 2023
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Yeqi Gao, Zhao Song, and Shenghao Xie · 2023
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Iterative α \alpha -(de) blending: A minimalist deterministic diffusion model
Eric Heitz, Laurent Belcour, and Thomas Chambon · 2023
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On sparse modern hopfield model
Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu, Chenwei Xu, Bo-Yu Chen, and Han Liu · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
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Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Multisample flow matching: Straightening flows with minibatch couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, and Ricky TQ Chen · 2023
Cited alongside, same era.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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The trade-off between universality and label efficiency of representations from contrastive learning
Yingyu Liang, Heshan Liu, Zhenmei Shi, Zhao Song, Zhuoyan Xu, and Junze Yin · 2024
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A tighter complexity analysis of sparsegpt
Xiaoyu Li, Yingyu Liang, Zhenmei Shi, and Zhao Song · 2024
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Fast second-order method for neural network under small treewidth setting
Xiaoyu Li, Jiangxuan Long, Zhao Song, and Tianyi Zhou · 2024
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Differential privacy mechanisms in neural tangent kernel regression
Yingyu Liang, Zhizhou Sha, Zhenmei Shi, and Zhao Song · 2024
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Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, and Somesh Jha · 2023
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Domain generalization via nuclear norm regularization
Zhenmei Shi, Yifei Ming, Ying Fan, Frederic Sala, and Yingyu Liang · 2023
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A theoretical analysis of nearest neighbor search on approximate near neighbor graph
Anshumali Shrivastava, Zhao Song, and Zhaozhuo Xu · 2023
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Ritwik Sinha, Zhao Song, and Tianyi Zhou · 2023
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Provable guarantees for neural networks via gradient feature learning
Zhenmei Shi, Junyi Wei, and Yingyu Liang · 2023
Cited alongside, same era.
An automatic learning rate schedule algorithm for achieving faster convergence and steeper descent
Zhao Song and Chiwun Yang · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Cited alongside, same era.
Yingyu Liang, Zhenmei Shi, Zhao Song, and Chiwun Yang · 2024
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Differential privacy of cross-attention with provable guarantee
Yingyu Liang, Zhenmei Shi, Zhao Song, and Yufa Zhou · 2024
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Tensor attention training: Provably efficient learning of higher-order transformers
Yingyu Liang, Zhenmei Shi, Zhao Song, and Yufa Zhou · 2024
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Quantum speedups for approximating the john ellipsoid
Xiaoyu Li, Zhao Song, and Junwei Yu · 2024
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Diff-foley: Synchronized video-to-audio synthesis with latent diffusion models
Simian Luo, Chuanhao Yan, Chenxu Hu, and Hang Zhao · 2024
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Score-based generative diffusion models for social recommendations
Chengyi Liu, Jiahao Zhang, Shijie Wang, Wenqi Fan, and Qing Li · 2024
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Llama 3.2: Revolutionizing edge ai and vision with open, customizable models
Meta · 2024
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Hello gpt-4o
OpenAI · 2024
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Beyond first-order tweedie: Solving inverse problems using latent diffusion
Litu Rout, Yujia Chen, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, and Wen-Sheng Chu · 2024
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Representational strengths and limitations of transformers
Clayton Sanford, Daniel J Hsu, and Matus Telgarsky · 2024
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Zhenmei Shi, Yifei Ming, Xuan-Phi Nguyen, Yingyu Liang, and Shafiq Joty · 2024
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Numerical pruning for efficient autoregressive models
Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Jing Liu, Ruiyi Zhang, Ryan A. Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang, and Jiuxiang Gu · 2024
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Why larger language models do in-context learning differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu, and Yingyu Liang · 2024
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Training multi-layer over-parametrized neural network in subquadratic time
Zhao Song, Lichen Zhang, and Ruizhe Zhang · 2024
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Uniform memory retrieval with larger capacity for modern hopfield models
Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao, and Han Liu · 2024
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STanhop: Sparse tandem hopfield model for memory-enhanced time series prediction
Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, and Han Liu · 2024
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Is a picture worth a thousand words? delving into spatial reasoning for vision language models
Jiayu Wang, Yifei Ming, Zhenmei Shi, Vibhav Vineet, Xin Wang, Sharon Li, and Neel Joshi · 2024
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Transformers are deep optimizers: Provable in-context learning for deep model training
Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu, Zhao Song, and Han Liu · 2024
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Omnicontrolnet: Dual-stage integration for conditional image generation
Yilin Wang, Haiyang Xu, Xiang Zhang, Zeyuan Chen, Zhizhou Sha, Zirui Wang, and Zhuowen Tu · 2024
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Bishop: Bi-directional cellular learning for tabular data with generalized sparse modern hopfield model
Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, and Han Liu · 2024
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Do large language models have compositional ability? an investigation into limitations and scalability
Zhuoyan Xu, Zhenmei Shi, and Yingyu Liang · 2024
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Towards few-shot adaptation of foundation models via multitask finetuning
Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, and Yingyu Liang · 2024
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Logicmp: A neuro-symbolic approach for encoding first-order logic constraints
Weidi Xu, Jingwei Wang, Lele Xie, Jianshan He, Hongting Zhou, Taifeng Wang, Xiaopei Wan, Jingdong Chen, Chao Qu, and Wei Chu · 2024
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Graph unlearning with efficient partial retraining
Jiahao Zhang · 2024
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Step-back prompting enables reasoning via abstraction in large language models
Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H Chi, Quoc V Le, and Denny Zhou · 2024
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Linear-time graph neural networks for scalable recommendations
Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu, Qing Li, Jian Pei, and Xiaorui Liu · 2024
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Minimax optimality of score-based diffusion models: Beyond the density lower bound assumptions
Kaihong Zhang, Heqi Yin, Feng Liang, and Jingbo Liu · 2024
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High-order matching for one-step shortcut diffusion models
Bo Chen, Chengyue Gong, Xiaoyu Li, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, and Mingda Wan · 2025
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Scaling law phenomena across regression paradigms: Multiple and kernel approaches
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Bypassing the exponential dependency: Looped transformers efficiently learn in-context by multi-step gradient descent
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Nrflow: Towards noise-robust generative modeling via second-order flow matching
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The computational limits of state-space models and mamba via the lens of circuit complexity
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Universal approximation of visual autoregressive transformers
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Flow matching achieves almost minimax optimal convergence
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