Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ravula, Sriram"'
Autor:
Voytan, Dimitri P., Ravula, Sriram, Ardel, Alexandru, Liebman, Elad, Dhara, Arnab, Sen, Mrinal K., Dimakis, Alexandros
Seismic images often contain both coherent and random artifacts which complicate their interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning method based on Deep Image Prior (DIP) which uses convolutional neural
Externí odkaz:
http://arxiv.org/abs/2405.17597
Autor:
Saxena, Divyanshu, Sharma, Nihal, Kim, Donghyun, Dwivedula, Rohit, Chen, Jiayi, Yang, Chenxi, Ravula, Sriram, Hu, Zichao, Akella, Aditya, Angel, Sebastian, Biswas, Joydeep, Chaudhuri, Swarat, Dillig, Isil, Dimakis, Alex, Godfrey, P. Brighten, Kim, Daehyeok, Rossbach, Chris, Wang, Gang
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are
Externí odkaz:
http://arxiv.org/abs/2312.07813
Autor:
Ravula, Sriram, Gorti, Varun, Deng, Bo, Chakraborty, Swagato, Pingenot, James, Mutnury, Bhyrav, Wallace, Doug, Winterberg, Doug, Klivans, Adam, Dimakis, Alexandros G.
A key problem when modeling signal integrity for passive filters and interconnects in IC packages is the need for multiple S-parameter measurements within a desired frequency band to obtain adequate resolution. These samples are often computationally
Externí odkaz:
http://arxiv.org/abs/2306.04001
Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction. We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI that leverages pre-trained d
Externí odkaz:
http://arxiv.org/abs/2306.03284
We study a new family of inverse problems for recovering representations of corrupted data. We assume access to a pre-trained representation learning network R(x) that operates on clean images, like CLIP. The problem is to recover the representation
Externí odkaz:
http://arxiv.org/abs/2110.07439
Autor:
Ravula, Sriram, Dimakis, Alexandros G.
We extend the Deep Image Prior (DIP) framework to one-dimensional signals. DIP is using a randomly initialized convolutional neural network (CNN) to solve linear inverse problems by optimizing over weights to fit the observed measurements. Our main f
Externí odkaz:
http://arxiv.org/abs/1904.08594