3 papers
cs.LG2022
Addressing Mistake Severity in Neural Networks with Semantic Knowledge
Natalie Abreu, Nathan Vaska, Victoria Helus
Robustness in deep neural networks and machine learning algorithms in general is an open research challenge. In particular, it is difficult to ensure algorithmic performance is mai…
cs.LG2022
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models
Nathan Vaska, Kevin Leahy, Victoria Helus
Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this…
cs.LG2020
Fast Training of Deep Neural Networks Robust to Adversarial Perturbations
Justin Goodwin, Olivia Brown, Victoria Helus
Deep neural networks are capable of training fast and generalizing well within many domains. Despite their promising performance, deep networks have shown sensitivities to perturba…