"generative modeling" Papers
47 papers found
Ambient Diffusion Omni: Training Good Models with Bad Data
Giannis Daras, Adrian Rodriguez-Munoz, Adam Klivans et al.
CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
XUCHEN FENG, Siyu Liao
Continuous Diffusion for Mixed-Type Tabular Data
Markus Mueller, Kathrin Gruber, Dennis Fok
Cross-fluctuation phase transitions reveal sampling dynamics in diffusion models
Sai Niranjan Ramachandran, Manish Krishan Lal, Suvrit Sra
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning
Yeongmin Kim, Kwanghyeon Lee, Minsang Park et al.
Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax
Ivan Butakov, Alexander Semenenko, Alexander Tolmachev et al.
Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling
Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin et al.
Energy-Weighted Flow Matching for Offline Reinforcement Learning
Shiyuan Zhang, Weitong Zhang, Quanquan Gu
Flow matching achieves almost minimax optimal convergence
Kenji Fukumizu, Taiji Suzuki, Noboru Isobe et al.
Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
Ryan Burgert, Yuancheng Xu, Wenqi Xian et al.
Informed Correctors for Discrete Diffusion Models
Yixiu Zhao, Jiaxin Shi, Feng Chen et al.
LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion
Biao Zhang, Peter Wonka
MET3R: Measuring Multi-View Consistency in Generated Images
Mohammad Asim, Christopher Wewer, Thomas Wimmer et al.
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Nayoung Kim, Seongsu Kim, Minsu Kim et al.
On the Feature Learning in Diffusion Models
Andi Han, Wei Huang, Yuan Cao et al.
Physics-Informed Diffusion Models
Jan-Hendrik Bastek, WaiChing Sun, Dennis Kochmann
TreeGen: A Bayesian Generative Model for Hierarchies
Marcel Kollovieh, Nils Fleischmann, Filippo Guerranti et al.
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
Yuchen Zhu, Tianrong Chen, Lingkai Kong et al.
Activation-Descent Regularization for Input Optimization of ReLU Networks
Hongzhan Yu, Sicun Gao
Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
Compositional Image Decomposition with Diffusion Models
Jocelin Su, Nan Liu, Yanbo Wang et al.
Data-free Distillation of Diffusion Models with Bootstrapping
Jiatao Gu, Chen Wang, Shuangfei Zhai et al.
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Aaron Lou, Chenlin Meng, Stefano Ermon
Generative Conditional Distributions by Neural (Entropic) Optimal Transport
Bao Nguyen, Binh Nguyen, Trung Hieu Nguyen et al.
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Jaehyeong Jo, Sung Ju Hwang
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
Manuel Brenner, Florian Hess, Georgia Koppe et al.
Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
Jaehoon Hahm, Junho Lee, Sunghyun Kim et al.
Latent Space Editing in Transformer-Based Flow Matching
Vincent Tao Hu, Wei Zhang, Meng Tang et al.
Learning Latent Space Hierarchical EBM Diffusion Models
Jiali Cui, Tian Han
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess et al.
Particle Denoising Diffusion Sampler
Angus Phillips, Hai-Dang Dau, Michael Hutchinson et al.
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua et al.
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
YAN WANG, Lihao Wang, Yuning Shen et al.
Reflected Flow Matching
Tianyu Xie, Yu Zhu, Longlin Yu et al.
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy et al.
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
Katherine Crowson, Stefan Baumann, Alex Birch et al.
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Sliced Wasserstein with Random-Path Projecting Directions
Khai Nguyen, Shujian Zhang, Tam Le et al.
Soft Prompt Generation for Domain Generalization
Shuanghao Bai, Yuedi Zhang, Wanqi Zhou et al.
SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space
Yunchen Li, Zhou Yu, Gaoqi He et al.
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
Elen Vardanyan, Sona Hunanyan, Tigran Galstyan et al.
StrWAEs to Invariant Representations
Hyunjong Lee, Yedarm Seong, Sungdong Lee et al.
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Yuyang Wang, Ahmed Elhag, Navdeep Jaitly et al.
Time Series Diffusion in the Frequency Domain
Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk et al.
Towards Neural Architecture Search through Hierarchical Generative Modeling
Lichuan Xiang, Łukasz Dudziak, Mohamed Abdelfattah et al.
What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions
raghav singhal, Mark Goldstein, Rajesh Ranganath