5 papers · 1 filter
WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps
Abbas Mammadov, Jerry Y. Huang, Justin Lin +5
Reward fine-tuning aims to update a pre-trained flow-based generative model to improve the downstream reward of its generated samples. Existing methods typically formulate this pro…
Exact Posterior Score Estimation for Solving Linear Inverse Problems
Abbas Mammadov, Ozgur Kara, Kaan Oktay +5
Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption. To use this prior to solve a linear inverse problem, one needs to…
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi +4
Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers incorporate a likelihood term to guide…
Guided Diffusion Sampling on Function Spaces with Applications to PDEs
Jiachen Yao, Abbas Mammadov, Julius Berner +4
We propose a general framework for conditional sampling in PDE-based inverse problems, targeting the recovery of whole solutions from extremely sparse or noisy measurements. This i…
Defining Neural Network Architecture through Polytope Structures of Dataset
Sangmin Lee, Abbas Mammadov, Jong Chul Ye
Current theoretical and empirical research in neural networks suggests that complex datasets require large network architectures for thorough classification, yet the precise nature…