14 citations · 33 across the 11 of their papers we have counts for
12 papers
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…
Test-time Fourier Style Calibration for Domain Generalization
Xingchen Zhao, Chang Liu, Anthony Sicilia +2
The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods…
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
Katherine Atwell, Anthony Sicilia, Seong Jae Hwang +1
Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside…
Point Cloud Augmentation with Weighted Local Transformations
Sihyeon Kim, Sanghyeok Lee, Dasol Hwang +3
Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach…
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…
Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning
Anthony Sicilia, Xingchen Zhao, Davneet Minhas +5
We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications. Many existing MDL techniques are model-dependent solutions which e…
Robust White Matter Hyperintensity Segmentation on Unseen Domain
Xingchen Zhao, Anthony Sicilia, Davneet Minhas +5
Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun a…
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…
Online Graph Completion: Multivariate Signal Recovery in Computer Vision
Won Hwa Kim, Mona Jalal, Seongjae Hwang +2
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision)…
Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders
Xin Ma, Guorong Wu, Seong Jae Hwang +1
Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant…
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Seong Jae Hwang, Zirui Tao, Won Hwa Kim +1
Generative models using neural network have opened a door to large-scale studies for various application domains, especially for studies that suffer from lack of real samples to ob…
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…