8 citations · 9 across the 7 of their papers we have counts for
7 papers
Generative Video Compression with Adaptive Score Distillation
Naifu Xue, Zhaoyang Jia, Haosen Li +7
Diffusion models provide strong generative capabilities for video compression at ultra-low bitrates. Existing diffusion-based video codecs adapt base models originally developed fo…
Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures
Xinhe Mu, Zaijiu Shang, Zhaoqi Zhou +4
Score-based diffusion models have achieved remarkable empirical success, motivating extensive theoretical work to establish their foundations. However, existing complexity bounds f…
UniFluids: Unified Neural Operator Learning with Conditional Flow-matching
Haosen Li, Qi Meng, Jiahao Li +4
Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of deep neural networks to learn solution operators o…
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
Rui Zhang, Qi Meng, Han Wan +3
Computational fluid dynamics (CFD) drives progress in numerous scientific and engineering fields, yet high-fidelity simulations remain computationally prohibitive. While machine le…
Riemannian Neural Geodesic Interpolant
Jiawen Wu, Bingguang Chen, Yuyi Zhou +3
Stochastic interpolants are efficient generative models that bridge two arbitrary probability density functions in finite time, enabling flexible generation from the source to the…
On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond
Bohan Wang, Huishuai Zhang, Qi Meng +3
This paper aims to clearly distinguish between Stochastic Gradient Descent with Momentum (SGDM) and Adam in terms of their convergence rates. We demonstrate that Adam achieves a fa…
Deciphering and integrating invariants for neural operator learning with various physical mechanisms
Rui Zhang, Qi Meng, Zhi-Ming Ma
Neural operators have been explored as surrogate models for simulating physical systems to overcome the limitations of traditional partial differential equation (PDE) solvers. Howe…