14 citations · 33 across the 11 of their papers we have counts for
4 papers · 1 filter
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
Anthony Sicilia, Katherine Atwell, Malihe Alikhani +1
Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking…
PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging
Anthony Sicilia, Xingchen Zhao, Anastasia Sosnovskikh +1
Application of deep neural networks to medical imaging tasks has in some sense become commonplace. Still, a "thorn in the side" of the deep learning movement is the argument that d…
Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve
Anthony Sicilia, Xingchen Zhao, Seong Jae Hwang
Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound on target error given by Ben-Da…
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
Seong Jae Hwang, Ronak Mehta, Hyunwoo J. Kim +1
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there a…