Zobrazeno 1 - 10
of 27 707
pro vyhledávání: '"RAVIKUMAR, A."'
Publikováno v:
2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) (No. DOE-ISU-0000830-10)
Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the data exchang
Externí odkaz:
http://arxiv.org/abs/2412.07917
Autor:
Ravikumar, Prashanth Thattai
Quantifying and aligning music AI model representations with human behavior is an important challenge in the field of MIR. This paper presents a platform for exploring the Direct alignment between AI music model Representations and Human Musical judg
Externí odkaz:
http://arxiv.org/abs/2411.14907
Autor:
Ravikumar, Deepak, Yeo, Alex, Zhu, Yiwen, Lakra, Aditya, Nagulapalli, Harsha, Ravindran, Santhosh Kumar, Suh, Steve, Dutta, Niharika, Fogarty, Andrew, Park, Yoonjae, Khushalani, Sumeet, Tarafdar, Arijit, Parekh, Kunal, Krishnan, Subru
Publikováno v:
Proceedings of the VLDB Endowment, Vol. 17, No. 7 ISSN 2150-8097, 2024
The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data at ease with hundreds of parallel executors. At Microsoft, we aim at p
Externí odkaz:
http://arxiv.org/abs/2411.11326
Autor:
Li, Bowen, Li, Zhaoyu, Du, Qiwei, Luo, Jinqi, Wang, Wenshan, Xie, Yaqi, Stepputtis, Simon, Wang, Chen, Sycara, Katia P., Ravikumar, Pradeep Kumar, Gray, Alexander G., Si, Xujie, Scherer, Sebastian
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks wit
Externí odkaz:
http://arxiv.org/abs/2411.00773
We consider the linear causal representation learning setting where we observe a linear mixing of $d$ unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as we
Externí odkaz:
http://arxiv.org/abs/2410.24059
Publikováno v:
Maldonado-Garcia, C., Zakeri, A., Frangi, A.F., Ravikumar, N. (2025). Predictive Intelligence in Medicine. PRIME 2024. LNCS, vol 15155, Springer, Cham
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherenc
Externí odkaz:
http://arxiv.org/abs/2410.14423
Existing approaches to differentiable structure learning of directed acyclic graphs (DAGs) rely on strong identifiability assumptions in order to guarantee that global minimizers of the acyclicity-constrained optimization problem identifies the true
Externí odkaz:
http://arxiv.org/abs/2410.06163
Autor:
Dai, Shenghong, Sohn, Jy-yong, Chen, Yicong, Alam, S M Iftekharul, Balakrishnan, Ravikumar, Banerjee, Suman, Himayat, Nageen, Lee, Kangwook
Continual Federated Learning (CFL) is essential for enabling real-world applications where multiple decentralized clients adaptively learn from continuous data streams. A significant challenge in CFL is mitigating catastrophic forgetting, where model
Externí odkaz:
http://arxiv.org/abs/2409.01585
Computer vision models are increasingly capable of classifying ovarian epithelial cancer subtypes, but they differ from pathologists by processing small tissue patches at a single resolution. Multi-resolution graph models leverage the spatial relatio
Externí odkaz:
http://arxiv.org/abs/2407.18105
Autor:
Rao, Ravipudi Venkata, shah, Ravikumar
Two simple yet powerful optimization algorithms, named the Best-Mean-Random (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper to handle both constrained and unconstrained optimization problems. These algorithms a
Externí odkaz:
http://arxiv.org/abs/2407.11149