Zobrazeno 1 - 10
of 1 923
pro vyhledávání: '"P., Ramasubramanian"'
Autor:
Das, Shuvangkar Chandra, Vu, Tuyen, Ramasubramanian, Deepak, Farantatos, Evangelos, Zhang, Jianhua, Ortmeyer, Thomas
This paper presents novel methods for tuning inverter controller gains using deep reinforcement learning (DRL). A Simulink-developed inverter model is converted into a dynamic link library (DLL) and integrated with a Python-based RL environment, leve
Externí odkaz:
http://arxiv.org/abs/2411.01451
Autor:
Niu, Luyao, Zhang, Hongchao, Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Clark, Andrew, Poovendran, Radha
Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environments or due to incorrect actions taken by an agent. When an erroneous dec
Externí odkaz:
http://arxiv.org/abs/2410.20288
Procedural activity videos often exhibit a long-tailed action distribution due to varying action frequencies and durations. However, state-of-the-art temporal action segmentation methods overlook the long tail and fail to recognize tail actions. Exis
Externí odkaz:
http://arxiv.org/abs/2408.09919
Autor:
Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Alexiou, Michail, Mertoguno, J. Sukarno, Bushnell, Linda, Poovendran, Radha
This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining
Externí odkaz:
http://arxiv.org/abs/2407.20466
Autor:
Abbas, Ammar N., Mehak, Shakra, Chasparis, Georgios C., Kelleher, John D., Guilfoyle, Michael, Leva, Maria Chiara, Ramasubramanian, Aswin K
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrates specific parts of the safety requirements, such as velocity constrai
Externí odkaz:
http://arxiv.org/abs/2407.02231
Autor:
Li, Yuetai, Xu, Zhangchen, Jiang, Fengqing, Niu, Luyao, Sahabandu, Dinuka, Ramasubramanian, Bhaskar, Poovendran, Radha
The remarkable performance of large language models (LLMs) in generation tasks has enabled practitioners to leverage publicly available models to power custom applications, such as chatbots and virtual assistants. However, the data used to train or f
Externí odkaz:
http://arxiv.org/abs/2406.12257
Autor:
Devaguptapu, Chaitanya, Aithal, Sumukh, Ramasubramanian, Shrinivas, Yamada, Moyuru, Kaul, Manohar
Self-supervised learning (SSL) with vision transformers (ViTs) has proven effective for representation learning as demonstrated by the impressive performance on various downstream tasks. Despite these successes, existing ViT-based SSL architectures d
Externí odkaz:
http://arxiv.org/abs/2406.12944
Autor:
Mohan, Rishi Kesav, Kanmani, Risheek Rakshit Sukumar, Ganesan, Krishna Anandan, Ramasubramanian, Nisha
In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical Processing (
Externí odkaz:
http://arxiv.org/abs/2405.17731
Autor:
Bhattacharjee, Bishwaranjan, Trivedi, Aashka, Muraoka, Masayasu, Ramasubramanian, Muthukumaran, Udagawa, Takuma, Gurung, Iksha, Pantha, Nishan, Zhang, Rong, Dandala, Bharath, Ramachandran, Rahul, Maskey, Manil, Bugbee, Kaylin, Little, Mike, Fancher, Elizabeth, Gerasimov, Irina, Mehrabian, Armin, Sanders, Lauren, Costes, Sylvain, Blanco-Cuaresma, Sergi, Lockhart, Kelly, Allen, Thomas, Grezes, Felix, Ansdell, Megan, Accomazzi, Alberto, El-Kurdi, Yousef, Wertheimer, Davis, Pfitzmann, Birgit, Ramis, Cesar Berrospi, Dolfi, Michele, de Lima, Rafael Teixeira, Vagenas, Panagiotis, Mukkavilli, S. Karthik, Staar, Peter, Vahidinia, Sanaz, McGranaghan, Ryan, Lee, Tsendgar
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks
Externí odkaz:
http://arxiv.org/abs/2405.10725
Autor:
Camarasa-Gómez, María, Gant, Stephen E., Ohad, Guy, Neaton, Jeffrey B., Ramasubramanian, Ashwin, Kronik, Leeor
Accurate prediction of electronic and optical excitations in van der Waals (vdW) materials is a long-standing challenge for density functional theory. The recently proposed Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH)
Externí odkaz:
http://arxiv.org/abs/2405.00643