Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Bowen, Edward"'
Autor:
Baksi, Krishanu Das, Soba, Elijah, Higgins, John J., Saini, Ravi, Wood, Jaden, Cook, Jane, Scott, Jack, Pudota, Nirmala, Weninger, Tim, Bowen, Edward, Bhattacharya, Sanmitra
Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy
Externí odkaz:
http://arxiv.org/abs/2409.15368
Autor:
Ahamed, Sayyed Farid, Banerjee, Soumya, Roy, Sandip, Quinn, Devin, Vucovich, Marc, Choi, Kevin, Rahman, Abdul, Hu, Alison, Bowen, Edward, Shetty, Sachin
Over the last few years, federated learning (FL) has emerged as a prominent method in machine learning, emphasizing privacy preservation by allowing multiple clients to collaboratively build a model while keeping their training data private. Despite
Externí odkaz:
http://arxiv.org/abs/2407.19119
Autor:
Wang, Cheng, Redino, Christopher, Clark, Ryan, Rahman, Abdul, Aguinaga, Sal, Murli, Sathvik, Nandakumar, Dhruv, Rao, Roland, Huang, Lanxiao, Radke, Daniel, Bowen, Edward
Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To bolster preparedness against potential attacks, organizations commonl
Externí odkaz:
http://arxiv.org/abs/2406.17576
Autor:
Wang, Cheng, Redino, Christopher, Rahman, Abdul, Clark, Ryan, Radke, Daniel, Cody, Tyler, Nandakumar, Dhruv, Bowen, Edward
Command and control (C2) channels are an essential component of many types of cyber attacks, as they enable attackers to remotely control their malware-infected machines and execute harmful actions, such as propagating malicious code across networks,
Externí odkaz:
http://arxiv.org/abs/2402.09200
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing
Externí odkaz:
http://arxiv.org/abs/2312.04587
Autor:
Banerjee, Soumya, Roy, Sandip, Ahamed, Sayyed Farid, Quinn, Devin, Vucovich, Marc, Nandakumar, Dhruv, Choi, Kevin, Rahman, Abdul, Bowen, Edward, Shetty, Sachin
The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to overfit upon the training data. MIAs are trained to distinguish between tr
Externí odkaz:
http://arxiv.org/abs/2312.00051
Autor:
Abburi, Harika, Roy, Kalyani, Suesserman, Michael, Pudota, Nirmala, Veeramani, Balaji, Bowen, Edward, Bhattacharya, Sanmitra
Recent Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing across wide range of styles and genres. However, such capabilities are prone to potential abuse, such as fake news g
Externí odkaz:
http://arxiv.org/abs/2311.03084