170 citations · 596 across the 62 of their papers we have counts for
7 papers · 1 filter
Variational Collaborative Learning for User Probabilistic Representation
Kenan Cui, Xu Chen, Jiangchao Yao +1
Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole informa…
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han, Gang Niu, Xingrui Yu +4
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels,…
Understanding VAEs in Fisher-Shannon Plane
Huangjie Zheng, Jiangchao Yao, Ya Zhang +2
In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the unce…
Masking: A New Perspective of Noisy Supervision
Bo Han, Jiangchao Yao, Gang Niu +4
It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth…
Variational Composite Autoencoders
Jiangchao Yao, Ivor Tsang, Ya Zhang
Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable. Previous variational autoencoders can be low…
Collaborative Learning for Weakly Supervised Object Detection
Jiajie Wang, Jiangchao Yao, Ya Zhang +1
Weakly supervised object detection has recently received much attention, since it only requires image-level labels instead of the bounding-box labels consumed in strongly supervise…
Degeneration in VAE: in the Light of Fisher Information Loss
Huangjie Zheng, Jiangchao Yao, Ya Zhang +1
While enormous progress has been made to Variational Autoencoder (VAE) in recent years, similar to other deep networks, VAE with deep networks suffers from the problem of degenerat…