211 citations · 940 across the 32 of their papers we have counts for
7 papers · 1 filter
ARM-Net: Adaptive Relation Modeling Network for Structured Data
Shaofeng Cai, Kaiping Zheng, Gang Chen +3
Relational databases are the de facto standard for storing and querying structured data, and extracting insights from structured data requires advanced analytics. Deep neural netwo…
Joining datasets via data augmentation in the label space for neural networks
Jake Zhao, Mingfeng Ou, Linji Xue +3
Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference. In this article, we are interested in systematic w…
A critical look at the current train/test split in machine learning
Jimin Tan, Jianan Yang, Sai Wu +2
The randomized or cross-validated split of training and testing sets has been adopted as the gold standard of machine learning for decades. The establishment of these split protoco…
LINDT: Tackling Negative Federated Learning with Local Adaptation
Hong Lin, Lidan Shou, Ke Chen +2
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without dis…
Effective and Efficient Dropout for Deep Convolutional Neural Networks
Shaofeng Cai, Yao Shu, Gang Chen +3
Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitti…
Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints
Shaofeng Cai, Gang Chen, Beng Chin Ooi +1
Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a hug…