no code implementations • 17 Feb 2023 • 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.
no code implementations • 5 Sep 2022 • Derek Everett, Andre T. Nguyen, Luke E. Richards, Edward Raff
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.
no code implementations • 4 May 2022 • Maksim E. Eren, Luke E. Richards, Manish Bhattarai, Roberto Yus, Charles Nicholas, Boian S. Alexandrov
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations.
no code implementations • 27 Dec 2021 • Gaoussou Youssouf Kebe, Luke E. Richards, Edward Raff, Francis Ferraro, Cynthia Matuszek
Learning to understand grounded language, which connects natural language to percepts, is a critical research area.
no code implementations • 23 Sep 2021 • Luke E. Richards, André Nguyen, Ryan Capps, Steven Forsythe, Cynthia Matuszek, Edward Raff
In this work we note that as studied, current transfer attack research has an unrealistic advantage for the attacker: the attacker has the exact same training data as the victim.
no code implementations • 1 Sep 2020 • Andre T. Nguyen, Luke E. Richards, Gaoussou Youssouf Kebe, Edward Raff, Kasra Darvish, Frank Ferraro, Cynthia Matuszek
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 items.