Zobrazeno 1 - 10
of 3 654
pro vyhledávání: '"Gogineni A"'
Autor:
Murali Krishna Enduri, Abdur Rashid Sangi, Satish Anamalamudi, Ramanadham Chandu Badrinath Manikanta, Kallam Yogeshvar Reddy, Panchumarthi Lovely Yeswanth, Suda Kiran Sai Reddy, Gogineni Asish Karthikeya
Publikováno v:
Mehran University Research Journal of Engineering and Technology, Vol 42, Iss 1, Pp 207-215 (2023)
With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is beca
Externí odkaz:
https://doaj.org/article/7e159bf44eff480b9d9127aeb7e38cb2
Autor:
Gogineni, Kailash, Dayapule, Sai Santosh, Gómez-Luna, Juan, Gogineni, Karthikeya, Wei, Peng, Lan, Tian, Sadrosadati, Mohammad, Mutlu, Onur, Venkataramani, Guru
Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, leading to execution latencies and prolonged training times. To overcome t
Externí odkaz:
http://arxiv.org/abs/2405.03967
Autor:
Jain, Shashwat, Krishnamurthy, Vikram, Rangaswamy, Muralidhar, Kang, Bosung, Gogineni, Sandeep
How to design a Markov Decision Process (MDP) based radar controller that makes small sacrifices in performance to mask its sensing plan from an adversary? The radar controller purposefully minimizes the Fisher information of its emissions so that an
Externí odkaz:
http://arxiv.org/abs/2403.15966
Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global mod
Externí odkaz:
http://arxiv.org/abs/2403.13108
Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples in an already trained machine learning model. This capability enables data holders to adhere strictly
Externí odkaz:
http://arxiv.org/abs/2403.07611
In modern radar systems, precise target localization using azimuth and velocity estimation is paramount. Traditional unbiased estimation methods have utilized gradient descent algorithms to reach the theoretical limits of the Cramer Rao Bound (CRB) f
Externí odkaz:
http://arxiv.org/abs/2401.11176
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex nature of these problem
Externí odkaz:
http://arxiv.org/abs/2309.03094
This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF) wherein network agents employ partial sharing of state parameters. We characterize the performance and convergence of the BR-CDF and study the impact of a coordinat
Externí odkaz:
http://arxiv.org/abs/2307.13906
Autor:
Gogineni, Emile1,2 (AUTHOR) andrew.koempel@osumc.edu, Chen, Hao2 (AUTHOR) ian.cruickshank@bison.howard.edu, Cruickshank Jr., Ian K.2 (AUTHOR) hengli@jhmi.edu, Koempel, Andrew1 (AUTHOR) aarush.gogineni@gmail.com, Gogineni, Aarush1 (AUTHOR), Li, Heng2 (AUTHOR) cdeville@jhmi.edu, Deville Jr., Curtiland2 (AUTHOR)
Publikováno v:
Cancers. Aug2024, Vol. 16 Issue 15, p2702. 13p.
Autor:
Gauthier, Francois, Gogineni, Vinay Chakravarthi, Werner, Stefan, Huang, Yih-Fang, Kuh, Anthony
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks (2023) 1-14
This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every ind
Externí odkaz:
http://arxiv.org/abs/2306.06399