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Found 6 papers, 0 papers with code
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition
Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks.
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training
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.
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations.
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech
Learning to understand grounded language, which connects natural language to percepts, is a critical research area.
Adversarial Transfer Attacks With Unknown Data and Class Overlap
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.
Practical Cross-modal Manifold Alignment for Grounded Language
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.
