4 citations · 13 across the 18 of their papers we have counts for
22 papers
Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies
Murali Indukuri, Mohammad Eskandari, Sree Nitya Kollu +2
Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies. Vision-Language Models (VLMs) ar…
Autonomous VR-Based Risk Detection for Situational Awareness in Dangerous Settings
Mohammad Eskandari, Murali Krishna Varma Indukuri, Stephanie M. Lukin +1
In high-risk environments such as disaster response, situational awareness depends not only on detecting hazards but also on communicating them clearly to human operators. Vision L…
Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Exam
Michael Majurski, Cynthia Matuszek
How carefully and unambiguously a question is phrased has a profound impact on the quality of the response, for Language Models (LMs) as well as people. While model capabilities co…
Limited Linguistic Diversity in Embodied AI Datasets
Selma Wanna, Agnes Luhtaru, Jonathan Salfity +4
Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate these systems remain poorly doc…
Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments
Samuel Nathanson, Rebecca Williams, Cynthia Matuszek
Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adv…
Topic Modeling and Link-Prediction for Material Property Discovery
Ryan C. Barron, Maksim E. Eren, Valentin Stanev +2
Link prediction infers missing or future relations between graph nodes, based on connection patterns. Scientific literature networks and knowledge graphs are typically large, spars…
Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corpora
Michael Majurski, Cynthia Matuszek
Language Models (LMs) continue to advance, improving response quality and coherence. Given Internet-scale training datasets, LMs have likely encountered much of what users may ask…
Matrix Factorization for Inferring Associations and Missing Links
Ryan Barron, Maksim E. Eren, Duc P. Truong +4
Missing link prediction is a method for network analysis, with applications in recommender systems, biology, social sciences, cybersecurity, information retrieval, and Artificial I…
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
Luke E. Richards, Jessie Yaros, Jasen Babcock +4
To create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different conditions. Many times, deployed machi…
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization
Ryan C. Barron, Maksim E. Eren, Olga M. Serafimova +2
Agentic Generative AI, powered by Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), Knowledge Graphs (KGs), and Vector Stores (VSs), represents a transformati…
HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning
Manish Bhattarai, Ryan Barron, Maksim Eren +8
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external document retrieval to provide domain-specific or up-to-date knowledge. The effect…
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta, Fred Lu, Andrew Barlow +5
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious acto…
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
Ryan C. Barron, Ves Grantcharov, Selma Wanna +8
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite…
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection
Ryan Barron, Maksim E. Eren, Manish Bhattarai +3
In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must sel…
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
Casey Kennington, Malihe Alikhani, Heather Pon-Barry +20
The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces…
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization
Ryan Barron, Maksim E. Eren, Manish Bhattarai +6
Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document o…
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
Tirth Patel, Fred Lu, Edward Raff +3
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0.1\% change can…
DDxT: Deep Generative Transformer Models for Differential Diagnosis
Mohammad Mahmudul Alam, Edward Raff, Tim Oates +1
Differential Diagnosis (DDx) is the process of identifying the most likely medical condition among the possible pathologies through the process of elimination based on evidence. An…
Does Starting Deep Learning Homework Earlier Improve Grades?
Edward Raff, Cynthia Matuszek
Intuitively, students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment. However, existing literature on the impact o…
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition
Luke E. Richards, Edward Raff, Cynthia Matuszek
Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks. An understudied question in that community is: for whom do the…
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech
Gaoussou Youssouf Kebe, Luke E. Richards, Edward Raff +2
Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primaril…
Adversarial Transfer Attacks With Unknown Data and Class Overlap
Luke E. Richards, André Nguyen, Ryan Capps +3
The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community. Th…