6 citations · 8 across the 7 of their papers we have counts for
7 papers
The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
Eric Yeats, Brendan Kennedy, Loc Truong +5
The hidden states of large language models (LLMs) are known to capture rich information relating to model knowledge and behavior that can be hard to extract from examination of inp…
What do Geometric Hallucination Detection Metrics Actually Measure?
Eric Yeats, John Buckheit, Sarah Scullen +9
Hallucination remains a barrier to deploying generative models in high-consequence applications. This is especially true in cases where external ground truth is not readily availab…
Proceedings of TDA: Applications of Topological Data Analysis to Data Science, Artificial Intelligence, and Machine Learning Workshop at SDM 2022
R. W. R. Darling, John A. Emanuello, Emilie Purvine +1
Topological Data Analysis (TDA) is a rigorous framework that borrows techniques from geometric and algebraic topology, category theory, and combinatorics in order to study the "sha…
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings
Andrew Golczynski, John A. Emanuello
Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approa…
Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021
John Emanuello, Kimberly Ferguson-Walter, Erik Hemberg +5
Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. Given the shortage of cybersecurity professionals, the ever-…
Robustness of ML-Enhanced IDS to Stealthy Adversaries
Vance Wong, John Emanuello
Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect…