Poster "generative modeling" Papers
87 papers found • Page 1 of 2
Conference
A Black-Box Debiasing Framework for Conditional Sampling
Han Cui, Jingbo Liu
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training
Myunsoo Kim, Donghyeon Ki, Seong-Woong Shim et al.
Aether: Geometric-Aware Unified World Modeling
Haoyi Zhu, Yifan Wang, Jianjun Zhou et al.
A solvable model of learning generative diffusion: theory and insights
Hugo Cui, Cengiz Pehlevan, Yue Lu
Assessing the quality of denoising diffusion models in Wasserstein distance: noisy score and optimal bounds
Vahan Arsenyan, Elen Vardanyan, Arnak Dalalyan
CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
XUCHEN FENG, Siyu Liao
Composition and Alignment of Diffusion Models using Constrained Learning
Shervin Khalafi, Ignacio Hounie, Dongsheng Ding et al.
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering
Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach
Contextual Thompson Sampling via Generation of Missing Data
Kelly W Zhang, Tianhui Cai, Hongseok Namkoong et al.
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
Deep MMD Gradient Flow without adversarial training
Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
Denoising with a Joint-Embedding Predictive Architecture
Chen Dengsheng, Jie Hu, Xiaoming Wei et al.
Diffusion Models and Gaussian Flow Matching: Two Sides of the Same Coin
Ruiqi Gao, Emiel Hoogeboom, Jonathan Heek et al.
DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model
Rui Yu, Xianghang Zhang, Runkai Zhao 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
Exponential Convergence Guarantees for Iterative Markovian Fitting
Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
Yinuo Ren, Haoxuan Chen, Yuchen Zhu et al.
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
Siyi Chen, Yixuan Jia, Qing Qu et al.
Flow matching achieves almost minimax optimal convergence
Kenji Fukumizu, Taiji Suzuki, Noboru Isobe et al.
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
Kyle Sargent, Kyle Hsu, Justin Johnson et al.
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
Luca Della Libera, Francesco Paissan, Cem Subakan et al.
Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models
Louis Bethune, David Vigouroux, Yilun Du et al.
Generating Physical Dynamics under Priors
Zihan Zhou, Xiaoxue Wang, Tianshu Yu
Generator Matching: Generative modeling with arbitrary Markov processes
Peter Holderrieth, Marton Havasi, Jason Yim et al.
Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
Ryan Burgert, Yuancheng Xu, Wenqi Xian et al.
High-Order Flow Matching: Unified Framework and Sharp Statistical Rates
Maojiang Su, Jerry Yao-Chieh Hu, Yi-Chen Lee et al.
Improving Neural Optimal Transport via Displacement Interpolation
Jaemoo Choi, Yongxin Chen, Jaewoong Choi
Improving Rectified Flow with Boundary Conditions
Xixi Hu, Runlong Liao, Bo Liu et al.
Informed Correctors for Discrete Diffusion Models
Yixiu Zhao, Jiaxin Shi, Feng Chen et al.
Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
Xiangxin Zhou, Yi Xiao, Haowei Lin et al.
ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
Sagnik Bhattacharya, Abhiram Gorle, Ahsan Bilal et al.
LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion
Biao Zhang, Peter Wonka
Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Zinan Lin, Enshu Liu, Xuefei Ning et al.
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.
Moment- and Power-Spectrum-Based Gaussianity Regularization for Text-to-Image Models
Jisung Hwang, Jaihoon Kim, Minhyuk Sung
Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen
Alessandro Palma, Till Richter, Hanyi Zhang 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
Progressive Compression with Universally Quantized Diffusion Models
Yibo Yang, Justus Will, Stephan Mandt
Proper Hölder-Kullback Dirichlet Diffusion: A Framework for High Dimensional Generative Modeling
Wanpeng Zhang, Yuhao Fang, Xihang Qiu et al.
REGEN: Learning Compact Video Embedding with (Re-)Generative Decoder
Yitian Zhang, Long Mai, Aniruddha Mahapatra et al.
Riemannian Flow Matching for Brain Connectivity Matrices via Pullback Geometry
Antoine Collas, Ce Ju, Nicolas Salvy et al.
Sampling 3D Molecular Conformers with Diffusion Transformers
J. Thorben Frank, Winfried Ripken, Gregor Lied et al.
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu et al.
Scaling Wearable Foundation Models
Girish Narayanswamy, Xin Liu, Kumar Ayush et al.
Steering Protein Family Design through Profile Bayesian Flow
Jingjing Gong, Yu Pei, Siyu Long et al.