5 citations · 9 across the 7 of their papers we have counts for
7 papers
Large Language Models as Realistic Microservice Trace Generators
Donghyun Kim, Sriram Ravula, Taemin Ha +3
Workload traces are essential to understand complex computer systems' behavior and manage processing and memory resources. Since real-world traces are hard to obtain, synthetic tra…
Seismic Image Denoising With A Physics-Constrained Deep Image Prior
Dimitri P. Voytan, Sriram Ravula, Alexandru Ardel +4
Seismic images often contain both coherent and random artifacts which complicate their interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning…
On a Foundation Model for Operating Systems
Divyanshu Saxena, Nihal Sharma, Donghyun Kim +15
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…
One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers
Sriram Ravula, Varun Gorti, Bo Deng +7
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…
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Sriram Ravula, Brett Levac, Yamin Arefeen +3
Magnetic resonance imaging (MRI) is a powerful medical imaging modality, but long acquisition times limit throughput, patient comfort, and clinical accessibility. Diffusion-based g…
Inverse Problems Leveraging Pre-trained Contrastive Representations
Sriram Ravula, Georgios Smyrnis, Matt Jordan +1
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…
One-dimensional Deep Image Prior for Time Series Inverse Problems
Sriram Ravula, Alexandros G. Dimakis
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 b…