Zobrazeno 1 - 10
of 466
pro vyhledávání: '"Kantarcioglu, Murat"'
Autor:
Wang, Chengen, Kantarcioglu, Murat
Consistency models are a new class of models that generate images by directly mapping noise to data, allowing for one-step generation and significantly accelerating the sampling process. However, their robustness against adversarial attacks has not y
Externí odkaz:
http://arxiv.org/abs/2410.19785
Autor:
Li, Zhuohang, Zhang, Jiaxin, Yan, Chao, Das, Kamalika, Kumar, Sricharan, Kantarcioglu, Murat, Malin, Bradley A.
Language models (LMs) are known to suffer from hallucinations and misinformation. Retrieval augmented generation (RAG) that retrieves verifiable information from an external knowledge corpus to complement the parametric knowledge in LMs provides a ta
Externí odkaz:
http://arxiv.org/abs/2410.08320
Autor:
Segovia-Dominguez, Ignacio, Chen, Yuzhou, Akcora, Cuneyt G., Zhen, Zhiwei, Kantarcioglu, Murat, Gel, Yulia R., Coskunuzer, Baris
Publikováno v:
LoG 2023
Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH), which furnishes an exclusive
Externí odkaz:
http://arxiv.org/abs/2401.13713
AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional probabilistic information to users. The majority of past research on AI and human decision-making has concentrat
Externí odkaz:
http://arxiv.org/abs/2309.10852
Autor:
Abdallah, Mustafa, Bagchi, Saurabh, Bopardikar, Shaunak D., Chan, Kevin, Gao, Xing, Kantarcioglu, Murat, Li, Congmiao, Liu, Peng, Zhu, Quanyan
Many of our critical infrastructure systems and personal computing systems have a distributed computing systems structure. The incentives to attack them have been growing rapidly as has their attack surface due to increasing levels of connectedness.
Externí odkaz:
http://arxiv.org/abs/2309.01281
Autor:
Mukherjee, Kunal, Wiedemeier, Joshua, Wang, Tianhao, Kim, Muhyun, Chen, Feng, Kantarcioglu, Murat, Jee, Kangkook
The opaqueness of ML-based security models hinders their broad adoption and consequently restricts transparent security operations due to their limited verifiability and explainability. To enhance the explainability of ML-based security models, we in
Externí odkaz:
http://arxiv.org/abs/2306.00934
The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets. These illegal activities often ut
Externí odkaz:
http://arxiv.org/abs/2306.07974
Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources. Recently, honeypots are being deployed for Internet of things (IoT) devices to lure attackers, and learn
Externí odkaz:
http://arxiv.org/abs/2305.00925
Topological data analysis (TDA) delivers invaluable and complementary information on the intrinsic properties of data inaccessible to conventional methods. However, high computational costs remain the primary roadblock hindering the successful applic
Externí odkaz:
http://arxiv.org/abs/2211.13708
Federated Learning (FL) is a distributed machine learning protocol that allows a set of agents to collaboratively train a model without sharing their datasets. This makes FL particularly suitable for settings where data privacy is desired. However, i
Externí odkaz:
http://arxiv.org/abs/2112.01274