15 citations · 29 across the 16 of their papers we have counts for
7 papers · 1 filter
SSplain: Sparse and Smooth Explainer for Retinopathy of Prematurity Classification
Elifnur Sunger, Tales Imbiriba, Peter Campbell +3
Neural networks are frequently used in medical diagnosis. However, due to their black-box nature, model explainers are used to help clinicians understand better and trust model out…
Hierarchical Temporal Point Process Modeling of Aggressive Behavior Onset in Psychiatric Inpatient Youth with Autism for Branching Factor Estimation
Michael Potter, Michael Everett, Deniz Erdogmus +3
Aggressive behavior in autistic inpatient youth often arises in temporally clustered bursts complicating efforts to distinguish external triggers from internal escalation. The samp…
Exploring Theory-Laden Observations in the Brain Basis of Emotional Experience
Christiana Westlin, Ashutosh Singh, Deniz Erdogmus +2
In the science of emotion, it is widely assumed that folk emotion categories form a biological and psychological typology, and studies are routinely designed and analyzed to identi…
Recursive Deep Inverse Reinforcement Learning
Paul Ghanem, Owen Howell, Michael Potter +4
Inferring an adversary's goals from exhibited behavior is crucial for counterplanning and non-cooperative multi-agent systems in domains like cybersecurity, military, and strategy…
Active Learning For Repairable Hardware Systems With Partial Coverage
Michael Potter, Beyza Kalkanlı, Deniz Erdoğmuş +1
Identifying the optimal diagnostic test and hardware system instance to infer reliability characteristics using field data is challenging, especially when constrained by fixed budg…
Temporal Point Process Modeling of Aggressive Behavior Onset in Psychiatric Inpatient Youths with Autism
Michael Potter, Michael Everett, Ashutosh Singh +6
Aggressive behavior, including aggression towards others and self-injury, occurs in up to 80% of children and adolescents with autism, making it a leading cause of behavioral healt…
Dependency-aware Maximum Likelihood Estimation for Active Learning
Beyza Kalkanli, Tales Imbiriba, Stratis Ioannidis +2
Active learning aims to efficiently build a labeled training set by strategically selecting samples to query labels from annotators. In this sequential process, each sample acquisi…