Zobrazeno 1 - 10
of 4 960
pro vyhledávání: '"P Suhas"'
Autor:
Sultania, Dewang, Lu, Zhaoyu, Naik, Twisha, Dernoncourt, Franck, Yoon, David Seunghyun, Sharma, Sanat, Bui, Trung, Gupta, Ashok, Vatsa, Tushar, Suresha, Suhas, Verma, Ishita, Belavadi, Vibha, Chen, Cheng, Friedrich, Michael
Domain specific question answering is an evolving field that requires specialized solutions to address unique challenges. In this paper, we show that a hybrid approach combining a fine-tuned dense retriever with keyword based sparse search methods si
Externí odkaz:
http://arxiv.org/abs/2412.03736
The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to Vision Transformers (ViT) across various computer v
Externí odkaz:
http://arxiv.org/abs/2411.17283
Autor:
Gupta, Vishakha, Winkel, Patrick, Thakur, Neel, van Vlaanderen, Peter, Wang, Yanhao, Ganjam, Suhas, Frunzio, Luigi, Schoelkopf, Robert J.
Lumped-element inductors are an integral component in the circuit QED toolbox. However, it is challenging to build inductors that are simultaneously compact, linear and low-loss with standard approaches that either rely on the geometric inductance of
Externí odkaz:
http://arxiv.org/abs/2411.12611
Autor:
Abed, Jehad, Kim, Jiheon, Shuaibi, Muhammed, Wander, Brook, Duijf, Boris, Mahesh, Suhas, Lee, Hyeonseok, Gharakhanyan, Vahe, Hoogland, Sjoerd, Irtem, Erdem, Lan, Janice, Schouten, Niels, Vijayakumar, Anagha Usha, Hattrick-Simpers, Jason, Kitchin, John R., Ulissi, Zachary W., van Vugt, Aaike, Sargent, Edward H., Sinton, David, Zitnick, C. Lawrence
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-
Externí odkaz:
http://arxiv.org/abs/2411.11783
A key development in the cybersecurity evaluations space is the work carried out by Meta, through their CyberSecEval approach. While this work is undoubtedly a useful contribution to a nascent field, there are notable features that limit its utility.
Externí odkaz:
http://arxiv.org/abs/2411.08813
Large language models (LLMs) have demonstrated remarkable performance in diverse tasks using zero-shot and few-shot prompting. Even though their capabilities of data synthesis have been studied well in recent years, the generated data suffers from a
Externí odkaz:
http://arxiv.org/abs/2411.08553
With the rise of marine exploration, underwater imaging has gained significant attention as a research topic. Underwater video enhancement has become crucial for real-time computer vision tasks in marine exploration. However, most existing methods fo
Externí odkaz:
http://arxiv.org/abs/2411.05886
Autor:
Manjunath, Yoga Suhas Kuruba, Szymanowski, Mathew, Wissborn, Austin, Li, Mushu, Zhao, Lian, Zhang, Xiao-Ping
Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-w
Externí odkaz:
http://arxiv.org/abs/2411.11894
Autor:
Manjunath, Yoga Suhas Kuruba, Wissborn, Austin, Szymanowski, Mathew, Li, Mushu, Zhao, Lian, Zhang, Xiao-Ping
In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector
Externí odkaz:
http://arxiv.org/abs/2411.05184
Autor:
Lunga, Langalibalele, Sreehari, Suhas
Machine learning models are prone to adversarial attacks, where inputs can be manipulated in order to cause misclassifications. While previous research has focused on techniques like Generative Adversarial Networks (GANs), there's limited exploration
Externí odkaz:
http://arxiv.org/abs/2411.03348