Zobrazeno 1 - 10
of 9 085
pro vyhledávání: '"Ravikumar, P"'
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
In this paper, we explore the properties of loss curvature with respect to input data in deep neural networks. Curvature of loss with respect to input (termed input loss curvature) is the trace of the Hessian of the loss with respect to the input. We
Externí odkaz:
http://arxiv.org/abs/2407.02747
Compressed video action recognition classifies video samples by leveraging the different modalities in compressed videos, namely motion vectors, residuals, and intra-frames. For this purpose, three neural networks are deployed, each dedicated to proc
Externí odkaz:
http://arxiv.org/abs/2407.02713
In this paper, we demonstrate a surprising capability of large language models (LLMs): given only input feature names and a description of a prediction task, they are capable of selecting the most predictive features, with performance rivaling the st
Externí odkaz:
http://arxiv.org/abs/2407.02694