39 citations · 169 across the 17 of their papers we have counts for
4 papers · 1 filter
gCastle: A Python Toolbox for Causal Discovery
Keli Zhang, Shengyu Zhu, Marcus Kalander +4
is an end-to-end Python toolbox for causal structure learning. It provides functionalities of generating data from either simulator or real-world dataset, learni…
Label-Aware Distribution Calibration for Long-tailed Classification
Chaozheng Wang, Shuzheng Gao, Cuiyun Gao +4
Real-world data usually present long-tailed distributions. Training on imbalanced data tends to render neural networks perform well on head classes while much worse on tail classes…
An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels
Yong Wen, Marcus Kalander, Chanfei Su +1
We consider the problem of training robust and accurate deep neural networks (DNNs) when subject to various proportions of noisy labels. Large-scale datasets tend to contain mislab…
Mask-GVAE: Blind Denoising Graphs via Partition
Jia Li, Mengzhou Liu, Honglei Zhang +4
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.…