2 citations · 4 across the 7 of their papers we have counts for
10 papers
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
Alex Wong, Allison Chen, Yangchao Wu +4
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encod…
An Adaptive Framework for Learning Unsupervised Depth Completion
Alex Wong, Xiaohan Fei, Byung-Woo Hong +1
We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for det…
Stochastic batch size for adaptive regularization in deep network optimization
Kensuke Nakamura, Stefano Soatto, Byung-Woo Hong
We propose a first-order stochastic optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. The adaptive re…
Adaptive Regularization via Residual Smoothing in Deep Learning Optimization
Junghee Cho, Junseok Kwon, Byung-Woo Hong
We present an adaptive regularization algorithm that can be effectively applied to the optimization problem in deep learning framework. Our regularization algorithm aims to take in…
Adaptive Weight Decay for Deep Neural Networks
Kensuke Nakamura, Byung-Woo Hong
Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model. One of the most popular re…
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Alex Wong, Byung-Woo Hong, Stefano Soatto
Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth. We follow a geometric approach that exploits abundant…