Zobrazeno 1 - 10
of 497
pro vyhledávání: '"P. Shakkottai"'
Autor:
Block, Jacob L., Srinivasan, Sundararajan, Collins, Liam, Mokhtari, Aryan, Shakkottai, Sanjay
The power of foundation models (FMs) lies in their capacity to learn highly expressive representations that can be adapted to a broad spectrum of tasks. However, these pretrained models require multiple stages of fine-tuning to become effective for d
Externí odkaz:
http://arxiv.org/abs/2410.22264
Autor:
Narasimhan, Sai Shankar, Agarwal, Shubhankar, Rout, Litu, Shakkottai, Sanjay, Chinchali, Sandeep P.
Generating realistic time series samples is crucial for stress-testing models and protecting user privacy by using synthetic data. In engineering and safety-critical applications, these samples must meet certain hard constraints that are domain-speci
Externí odkaz:
http://arxiv.org/abs/2410.12652
Autor:
Rout, Litu, Chen, Yujia, Ruiz, Nataniel, Caramanis, Constantine, Shakkottai, Sanjay, Chu, Wen-Sheng
Generative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing. This paper addresses two key tasks: (i) inversion and (ii) editing of a real image using stochastic equi
Externí odkaz:
http://arxiv.org/abs/2410.10792
Restless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Markov chain and each state transition generates a scalar reward. However,
Externí odkaz:
http://arxiv.org/abs/2410.05527
Autor:
An, Qing, Pandey, Divyanshu, Doost-Mohammady, Rahman, Sabharwal, Ashutosh, Shakkottai, Srinivas
An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A k
Externí odkaz:
http://arxiv.org/abs/2407.09706
Autor:
Carleton, Jeremy, Vijaykumar, Prathik, Saxena, Divyanshu, Narasimha, Dheeraj, Shakkottai, Srinivas, Akella, Aditya
We address the challenge of zeroth-order online convex optimization where the objective function's gradient exhibits sparsity, indicating that only a small number of dimensions possess non-zero gradients. Our aim is to leverage this sparsity to obtai
Externí odkaz:
http://arxiv.org/abs/2407.06325
We address the resource allocation challenges in NextG cellular radio access networks (RAN), where heterogeneous user applications demand guarantees on throughput and service regularity. We leverage the Whittle indexability property to decompose the
Externí odkaz:
http://arxiv.org/abs/2406.01888
Autor:
Rout, Litu, Chen, Yujia, Ruiz, Nataniel, Kumar, Abhishek, Caramanis, Constantine, Shakkottai, Sanjay, Chu, Wen-Sheng
We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the abs
Externí odkaz:
http://arxiv.org/abs/2405.17401
Autor:
Bura, Archana, Bobbili, Sarat Chandra, Rameshkumar, Shreyas, Rengarajan, Desik, Kalathil, Dileep, Shakkottai, Srinivas
Media streaming is the dominant application over wireless edge (access) networks. The increasing softwarization of such networks has led to efforts at intelligent control, wherein application-specific actions may be dynamically taken to enhance the u
Externí odkaz:
http://arxiv.org/abs/2404.07315
A striking property of transformers is their ability to perform in-context learning (ICL), a machine learning framework in which the learner is presented with a novel context during inference implicitly through some data, and tasked with making a pre
Externí odkaz:
http://arxiv.org/abs/2402.11639