211 citations · 657 across the 19 of their papers we have counts for
31 papers
Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal
Lei Zhu, Zhaojing Luo, Wei Wang +3
Deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-doma…
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…
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment
Can Cui, Wei Wang, Meihui Zhang +3
Alphas are stock prediction models capturing trading signals in a stock market. A set of effective alphas can generate weakly correlated high returns to diversify the risk. Existin…
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…