47 citations · 88 across the 13 of their papers we have counts for
13 papers
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions
Edward Y. Chang
The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning. In the fie…
Hypernetwork-Based Augmentation
Chih-Yang Chen, Che-Han Chang
Data augmentation is an effective technique to improve the generalization of deep neural networks. Recently, AutoAugment proposed a well-designed search space and a search algorith…
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
Po-Wei Wu, Yu-Jing Lin, Che-Han Chang +2
Multi-domain image-to-image translation has gained increasing attention recently. Previous methods take an image and some target attributes as inputs and generate an output image w…
Effective Medical Test Suggestions Using Deep Reinforcement Learning
Yang-En Chen, Kai-Fu Tang, Yu-Shao Peng +1
Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy. In this work, we show that an agent can learn to suggest ef…
G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
Fu-Chieh Chang, Hao-Jen Wang, Chun-Nan Chou +1
Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may…
KG-GAN: Knowledge-Guided Generative Adversarial Networks
Che-Han Chang, Chun-Hsien Yu, Szu-Ying Chen +1
Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject…
MBS: Macroblock Scaling for CNN Model Reduction
Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang
In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size. MBS adaptively reduces each CNN macro…
BRIEF: Backward Reduction of CNNs with Information Flow Analysis
Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang
This paper proposes BRIEF, a backward reduction algorithm that explores compact CNN-model designs from the information flow perspective. This algorithm can remove substantial non-z…
EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks
Sheng-Wei Chen, Chun-Nan Chou, Edward Y. Chang
For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate…
Distributed Training Large-Scale Deep Architectures
Shang-Xuan Zou, Chun-Yen Chen, Jui-Lin Wu +6
Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorith…
Representation Learning on Large and Small Data
Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang +2
Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation. This book chapter presented the im…
A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories
Cheng Yang, Maosong Sun, Wayne Xin Zhao +2
The accelerated growth of mobile trajectories in location-based services brings valuable data resources to understand users' moving behaviors. Apart from recording the trajectory d…
Errata: Distant Supervision for Relation Extraction with Matrix Completion
Miao Fan, Deli Zhao, Qiang Zhou +3
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and no…