61 citations · 321 across the 27 of their papers we have counts for
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cs.LG2019
Advocacy Learning: Learning through Competition and Class-Conditional Representations
Ian Fox, Jenna Wiens
We introduce advocacy learning, a novel supervised training scheme for attention-based classification problems. Advocacy learning relies on a framework consisting of two connected…
cs.CY2019★ 44 cited
Automatically Evaluating Balance: A Machine Learning Approach
Tian Bao, Brooke N. Klatt, Susan L. Whitney +2
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the fea…
cs.LG2019★ 10 cited
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series
Jeeheh Oh, Jiaxuan Wang, Shengpu Tang +2
Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part…