Zobrazeno 1 - 10
of 13 359
pro vyhledávání: '"Noorbakhsh A"'
Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to mitigate both the security and privacy attacks, and maintain task utility
Externí odkaz:
http://arxiv.org/abs/2412.11066
Autor:
Hossain, Elias, Nuzhat, Tasfia, Masum, Shamsul, Rahimi, Shahram, Mittal, Sudip, Golilarz, Noorbakhsh Amiri
Accurate classification of cancer-related medical abstracts is crucial for healthcare management and research. However, obtaining large, labeled datasets in the medical domain is challenging due to privacy concerns and the complexity of clinical data
Externí odkaz:
http://arxiv.org/abs/2410.15198
In this paper, we discuss learning algorithms and their importance in different types of applications which includes training to identify important patterns and features in a straightforward, easy-to-understand manner. We will review the main concept
Externí odkaz:
http://arxiv.org/abs/2410.09186
Autor:
Neupane, Subash, Hossain, Elias, Keith, Jason, Tripathi, Himanshu, Ghiasi, Farbod, Golilarz, Noorbakhsh Amiri, Amirlatifi, Amin, Mittal, Sudip, Rahimi, Shahram
This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot system built using Retrieval Augmented Generation (RAG) pipelines to enhance the user experience and access to information within academic settings.The objective of BARKPLUG
Externí odkaz:
http://arxiv.org/abs/2405.08120
Autor:
Neupane, Subash, Mitra, Shaswata, Mittal, Sudip, Golilarz, Noorbakhsh Amiri, Rahimi, Shahram, Amirlatifi, Amin
Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive responses are v
Externí odkaz:
http://arxiv.org/abs/2403.08607
Machine learning (ML) is vulnerable to inference (e.g., membership inference, property inference, and data reconstruction) attacks that aim to infer the private information of training data or dataset. Existing defenses are only designed for one spec
Externí odkaz:
http://arxiv.org/abs/2403.02116
This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images. Given the inherent complexity of generative models and the absence of a standardized procedure for thei
Externí odkaz:
http://arxiv.org/abs/2402.17204
Autor:
Khatib, Hassan S. Al, Neupane, Subash, Manchukonda, Harish Kumar, Golilarz, Noorbakhsh Amiri, Mittal, Sudip, Amirlatifi, Amin, Rahimi, Shahram
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of heal
Externí odkaz:
http://arxiv.org/abs/2402.12608
Publikováno v:
Journal of Enterprising Communities: People and Places in the Global Economy, 2024, Vol. 18, Issue 6, pp. 1384-1414.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JEC-04-2024-0064
Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent studies show that an adversary can still be possible to infer private
Externí odkaz:
http://arxiv.org/abs/2312.06989