8 citations · 23 across the 6 of their papers we have counts for
7 papers
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…
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…
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training
Derek Everett, Andre T. Nguyen, Luke E. Richards +1
The quantification of uncertainty is important for the adoption of machine learning, especially to reject out-of-distribution (OOD) data back to human experts for review. Yet progr…
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation
Maksim E. Eren, Luke E. Richards, Manish Bhattarai +3
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendation…
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…
Practical Cross-modal Manifold Alignment for Grounded Language
Andre T. Nguyen, Luke E. Richards, Gaoussou Youssouf Kebe +4
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world it…