Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Pandey, Kushagra"'
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
Constructing fast samplers for unconditional diffusion and flow-matching models has received much attention recently; however, existing methods for solving inverse problems, such as super-resolution, inpainting, or deblurring, still require hundreds
Externí odkaz:
http://arxiv.org/abs/2405.17673
Autor:
Manduchi, Laura, Pandey, Kushagra, Bamler, Robert, Cotterell, Ryan, Däubener, Sina, Fellenz, Sophie, Fischer, Asja, Gärtner, Thomas, Kirchler, Matthias, Kloft, Marius, Li, Yingzhen, Lippert, Christoph, de Melo, Gerard, Nalisnick, Eric, Ommer, Björn, Ranganath, Rajesh, Rudolph, Maja, Ullrich, Karen, Broeck, Guy Van den, Vogt, Julia E, Wang, Yixin, Wenzel, Florian, Wood, Frank, Mandt, Stephan, Fortuin, Vincent
The field of deep generative modeling has grown rapidly and consistently over the years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models
Externí odkaz:
http://arxiv.org/abs/2403.00025
Diffusion models suffer from slow sample generation at inference time. Despite recent efforts, improving the sampling efficiency of stochastic samplers for diffusion models remains a promising direction. We propose Splitting Integrators for fast stoc
Externí odkaz:
http://arxiv.org/abs/2402.07211
Diffusion models suffer from slow sample generation at inference time. Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class of diffusion models is a promising direction. We propose two complement
Externí odkaz:
http://arxiv.org/abs/2310.07894
Autor:
Pandey, Kushagra, Mandt, Stephan
Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or sim
Externí odkaz:
http://arxiv.org/abs/2303.01748
Autor:
Mukherjee, Rudra Narayan, Pandey, Kushagra, Kumar, Akshay Ganesh, Phalak, Manoj, Borkar, Sachin, Garg, Kanwaljeet, Chandra, Sarat P., Kale, Shashank Sharad
Publikováno v:
In Journal of Clinical Neuroscience November 2024 129
Autor:
Goyal, Sarvesh, Pandey, Kushagra, Kedia, Shweta, Sebastian, Leve Joseph Devarajan, Agrawal, Deepak
Publikováno v:
In Journal of Clinical Neuroscience October 2024 128
Autor:
Alam, Intekhab, Garg, Kanwaljeet, Kumar, Akshay Ganesh, Raheja, Amol, Shah, Het, Pandey, Kushagra, Sharma, Ravi, Mishra, Shashwat, Tandon, Vivek, Singh, Manmohan, Ahmad, Faiz U., Suri, Ashish, Kale, Shashank Sharad
Publikováno v:
In World Neurosurgery September 2024 189:e61-e68
Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. On the other hand, standard Vari
Externí odkaz:
http://arxiv.org/abs/2201.00308