Search Results for author: Noah Ziems

Found 6 papers, 1 papers with code

Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection

no code implementations30 Oct 2023 Noah Ziems, Gang Liu, John Flanagan, Meng Jiang

Finally, we show LLM generated decision tree explanations correlate highly with human ratings of readability, quality, and use of background knowledge while simultaneously providing better understanding of decision boundaries.

Network Intrusion Detection

Embedding Mental Health Discourse for Community Recommendation

no code implementations8 Jul 2023 Hy Dang, Bang Nguyen, Noah Ziems, Meng Jiang

Our paper investigates the use of discourse embedding techniques to develop a community recommendation system that focuses on mental health support groups on social media.

Collaborative Filtering

Large Language Models are Built-in Autoregressive Search Engines

1 code implementation16 May 2023 Noah Ziems, Wenhao Yu, Zhihan Zhang, Meng Jiang

To overcome this limitation, recent autoregressive search engines replace the dual-encoder architecture by directly generating identifiers for relevant documents in the candidate pool.

Open-Domain Question Answering Retrieval

CodeDSI: Differentiable Code Search

no code implementations1 Oct 2022 Usama Nadeem, Noah Ziems, Shaoen Wu

In an effort to improve the performance of code search, we have investigated docid representation strategies, impact of tokenization on docid structure, and dataset sizes on overall code search performance.

Code Generation Code Search +2

Security Vulnerability Detection Using Deep Learning Natural Language Processing

no code implementations6 May 2021 Noah Ziems, Shaoen Wu

Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades.

Transfer Learning Vulnerability Detection

Automated Primary Hyperparathyroidism Screening with Neural Networks

no code implementations6 May 2021 Noah Ziems, Shaoen Wu, Jim Norman

Primary Hyperparathyroidism(PHPT) is a relatively common disease, affecting about one in every 1, 000 adults.


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