Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Kannan, Rajgopal"'
Graphs play a crucial role in data mining and machine learning, representing real-world objects and interactions. As graph datasets grow, managing large, decentralized subgraphs becomes essential, particularly within federated learning frameworks. Th
Externí odkaz:
http://arxiv.org/abs/2410.14010
Heterophilous graphs, where dissimilar nodes tend to connect, pose a challenge for graph neural networks (GNNs) as their superior performance typically comes from aggregating homophilous information. Increasing the GNN depth can expand the scope (i.e
Externí odkaz:
http://arxiv.org/abs/2409.06998
Attention mechanisms are critically important in the advancement of synthetic aperture radar (SAR) automatic target recognition (ATR) systems. Traditional SAR ATR models often struggle with the noisy nature of the SAR data, frequently learning from b
Externí odkaz:
http://arxiv.org/abs/2409.00473
Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the bottleneck kernel of sparse tensor decomposition. In this work, we propose a GPU-based algorithm design to address the key challenges in accelerating spMTTKRP computation, including
Externí odkaz:
http://arxiv.org/abs/2405.08470
Graph neural networks (GNNs) have recently empowered various novel computer vision (CV) tasks. In GNN-based CV tasks, a combination of CNN layers and GNN layers or only GNN layers are employed. This paper introduces GCV-Turbo, a domain-specific accel
Externí odkaz:
http://arxiv.org/abs/2404.07188
Autor:
Wickramasinghe, Sachini, Parikh, Dhruv, Zhang, Bingyi, Kannan, Rajgopal, Prasanna, Viktor, Busart, Carl
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique used in military applications like remote-sensing image recognition. Vision Transformers (ViTs) are the current state-of-the-art in various computer vision applicati
Externí odkaz:
http://arxiv.org/abs/2404.04527
Deep Learning (DL) Models for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), while delivering improved performance, have been shown to be quite vulnerable to adversarial attacks. Existing works improve robustness by training model
Externí odkaz:
http://arxiv.org/abs/2404.03225
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems. An adversarial attack can deceive the classifier into making incorrect pr
Externí odkaz:
http://arxiv.org/abs/2403.18318
Vision Transformers (ViTs) have achieved state-of-the-art accuracy on various computer vision tasks. However, their high computational complexity prevents them from being applied to many real-world applications. Weight and token pruning are two well-
Externí odkaz:
http://arxiv.org/abs/2403.14047
Publikováno v:
2023 IEEE High Performance Extreme Computing Conference (HPEC), 2023, pp. 1-7
Deep neural networks (DNNs) have proven to be effective models for accurate Memory Access Prediction (MAP), a critical task in mitigating memory latency through data prefetching. However, existing DNN-based MAP models suffer from the challenges such
Externí odkaz:
http://arxiv.org/abs/2402.13441