Zobrazeno 1 - 10
of 8 809
pro vyhledávání: '"A Mardani"'
Autor:
Daras, Giannis, Nie, Weili, Kreis, Karsten, Dimakis, Alex, Mardani, Morteza, Kovachki, Nikola Borislavov, Vahdat, Arash
Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these problems, in this paper, we view frames as continuous functions in the 2D s
Externí odkaz:
http://arxiv.org/abs/2410.16152
Autor:
Pandey, Kushagra, Pathak, Jaideep, Xu, Yilun, Mandt, Stephan, Pritchard, Michael, Vahdat, Arash, Mardani, Morteza
Diffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unclear. In this work, we show that traditional diffusion and flow-matchi
Externí odkaz:
http://arxiv.org/abs/2410.14171
Autor:
Fotiadis, Stathi, Brenowitz, Noah, Geffner, Tomas, Cohen, Yair, Pritchard, Michael, Vahdat, Arash, Mardani, Morteza
Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolving small-scale details poses significant challenges due to: (i) misalig
Externí odkaz:
http://arxiv.org/abs/2410.19814
It is well-known that the standard bulk-boundary correspondence does not hold for non-Hermitian systems in which also new phenomena such as exceptional points do occur. Here we study by analytical and numerical means a paradigmatic one-dimensional no
Externí odkaz:
http://arxiv.org/abs/2410.01542
Autor:
Pathak, Jaideep, Cohen, Yair, Garg, Piyush, Harrington, Peter, Brenowitz, Noah, Durran, Dale, Mardani, Morteza, Vahdat, Arash, Xu, Shaoming, Kashinath, Karthik, Pritchard, Michael
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosph
Externí odkaz:
http://arxiv.org/abs/2408.10958
Score Distillation Sampling (SDS) has been pivotal for leveraging pre-trained diffusion models in downstream tasks such as inverse problems, but it faces two major challenges: $(i)$ mode collapse and $(ii)$ latent space inversion, which become more p
Externí odkaz:
http://arxiv.org/abs/2406.16683
Autor:
Manshausen, Peter, Cohen, Yair, Pathak, Jaideep, Pritchard, Mike, Garg, Piyush, Mardani, Morteza, Kashinath, Karthik, Byrne, Simon, Brenowitz, Noah
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retra
Externí odkaz:
http://arxiv.org/abs/2406.16947
Autor:
Akyash, Mohammad, Kamali, Hadi Mardani
The rise of instruction-tuned Large Language Models (LLMs) marks a significant advancement in artificial intelligence (AI) (tailored to respond to specific prompts). Despite their popularity, applying such models to debug security vulnerabilities in
Externí odkaz:
http://arxiv.org/abs/2405.12347
Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose a scene into visual primitives - denoted as dense blob representation
Externí odkaz:
http://arxiv.org/abs/2405.08246
Autor:
Akyash, Mohammad, Kamali, Hadi Mardani
Automating hardware (HW) security vulnerability detection and mitigation during the design phase is imperative for two reasons: (i) It must be before chip fabrication, as post-fabrication fixes can be costly or even impractical; (ii) The size and com
Externí odkaz:
http://arxiv.org/abs/2404.16651