454 citations · 2.5k across the 90 of their papers we have counts for
25 papers · 1 filter
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Yujie Pan, Jiangchao Yao, Bo Han +3
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scena…
Modeling Adversarial Noise for Adversarial Training
Dawei Zhou, Nannan Wang, Bo Han +1
Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks. Motivated by the fact that adve…
Instance-dependent Label-noise Learning under a Structural Causal Model
Yu Yao, Tongliang Liu, Mingming Gong +3
Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, r…
Local Reweighting for Adversarial Training
Ruize Gao, Feng Liu, Kaiwen Zhou +3
Instances-reweighted adversarial training (IRAT) can significantly boost the robustness of trained models, where data being less/more vulnerable to the given attack are assigned sm…
Towards Defending against Adversarial Examples via Attack-Invariant Features
Dawei Zhou, Tongliang Liu, Bo Han +3
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously ev…
Understanding and Improving Early Stopping for Learning with Noisy Labels
Yingbin Bai, Erkun Yang, Bo Han +5
The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-the-art label-noise learning methods. To exploit this property, the early stopping trick,…