Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Mohan, C Krishna"'
Computing the loss gradient via backpropagation consumes considerable energy during deep learning (DL) model training. In this paper, we propose a novel approach to efficiently compute DL models' gradients to mitigate the substantial energy overhead
Externí odkaz:
http://arxiv.org/abs/2406.07332
Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model while preserving the privacy of their sensitive data. Nevertheless, the inherent decentralized and data-opaque ch
Externí odkaz:
http://arxiv.org/abs/2404.04139
Autor:
Dayal, Aveen, B., Vimal K., Cenkeramaddi, Linga Reddy, Mohan, C. Krishna, Kumar, Abhinav, Balasubramanian, Vineeth N
Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous me
Externí odkaz:
http://arxiv.org/abs/2311.08503
Autor:
Makwana, Dhruv, Nag, Subhrajit, Susladkar, Onkar, Deshmukh, Gayatri, R, Sai Chandra Teja, Mittal, Sparsh, Mohan, C Krishna
Publikováno v:
volume 13, pages 865-875, year 2022
We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images. ACLNet uses both deep neural network and machine learning (ML) algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as the b
Externí odkaz:
http://arxiv.org/abs/2207.06277
Publikováno v:
Volume 142, 2022, 103720, ISSN 0166-3615
As the integration density and design intricacy of semiconductor wafers increase, the magnitude and complexity of defects in them are also on the rise. Since the manual inspection of wafer defects is costly, an automated artificial intelligence (AI)
Externí odkaz:
http://arxiv.org/abs/2207.00960
Autor:
Kumar, K. Naveen, Roy, Debaditya, Suman, Thakur Ashutosh, Vishnu, Chalavadi, Mohan, C. Krishna
Publikováno v:
In Pattern Recognition November 2024 155
With the advancements made in deep learning, computer vision problems like object detection and segmentation have seen a great improvement in performance. However, in many real-world applications such as autonomous driving vehicles, the risk associat
Externí odkaz:
http://arxiv.org/abs/2108.03614
Despite the high quality performance of the deep neural network in real-world applications, they are susceptible to minor perturbations of adversarial attacks. This is mostly undetectable to human vision. The impact of such attacks has become extreme
Externí odkaz:
http://arxiv.org/abs/2101.06092
Publikováno v:
In Soil Dynamics and Earthquake Engineering February 2024 177
Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior at intersections. Particularly, in developing countries with high vehicle density, mixed traffic type, and lane-less driving behavior, i
Externí odkaz:
http://arxiv.org/abs/2008.00827