87 citations · 112 across the 8 of their papers we have counts for
4 papers · 1 filter
Gradient-Coherent Strong Regularization for Deep Neural Networks
Dae Hoon Park, Chiu Man Ho, Yi Chang +1
Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are co…
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Dae Hoon Park, Yi Chang
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the m…
Sequenced-Replacement Sampling for Deep Learning
Chiu Man Ho, Dae Hoon Park, Wei Yang +1
We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-bat…
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Kai Xu, Dae Hoon Park, Chang Yi +1
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting. A natural idea is to visualize…