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pro vyhledávání: '"Kowshik, Suhas"'
Large language models (LLMs) have demonstrated remarkable performance in diverse tasks using zero-shot and few-shot prompting. Even though their capabilities of data synthesis have been studied well in recent years, the generated data suffers from a
Externí odkaz:
http://arxiv.org/abs/2411.08553
Autor:
Kowshik, Suhas Subramanya
Non-asymptotic understanding of information theoretic and algorithmic limits of estimation in statistical problems is indispensable for practical applications in engineering. There are two broad approaches to this end. The first: tools and advances i
In this work, we consider the problem of collaborative multi-user reinforcement learning. In this setting there are multiple users with the same state-action space and transition probabilities but with different rewards. Under the assumption that the
Externí odkaz:
http://arxiv.org/abs/2210.05355
Autor:
Kowshik, Suhas S
Many-user MAC is an important model for understanding energy efficiency of massive random access in 5G and beyond. Introduced in Polyanskiy'2017 for the AWGN channel, subsequent works have provided improved bounds on the asymptotic minimum energy-per
Externí odkaz:
http://arxiv.org/abs/2201.00866
We consider the setting of vector valued non-linear dynamical systems $X_{t+1} = \phi(A^* X_t) + \eta_t$, where $\eta_t$ is unbiased noise and $\phi : \mathbb{R} \to \mathbb{R}$ is a known link function that satisfies certain {\em expansivity propert
Externí odkaz:
http://arxiv.org/abs/2105.11558
We consider the problem of estimating a linear time-invariant (LTI) dynamical system from a single trajectory via streaming algorithms, which is encountered in several applications including reinforcement learning (RL) and time-series analysis. While
Externí odkaz:
http://arxiv.org/abs/2103.05896
Gas turbine combustion chambers contain numerous smallscale features that help to dampen acoustic waves and alter the acoustic mode shapes. This damping helps to alleviate problems such as thermoacoustic instabilities. During computational fluid dyna
Externí odkaz:
http://arxiv.org/abs/2012.00335
Akademický článek
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-83).
Wireless networks in the near
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-83).
Wireless networks in the near
Externí odkaz:
https://hdl.handle.net/1721.1/122873
We discuss the problem of designing channel access architectures for enabling fast, low-latency, grant-free and uncoordinated uplink for densely packed wireless nodes. Specifically, we study random-access codes, previously introduced for the AWGN mul
Externí odkaz:
http://arxiv.org/abs/1907.09448