ICML 2024 "generative modeling" Papers
26 papers found
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.
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.
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