791 citations · 2.5k across the 66 of their papers we have counts for
11 papers · 1 filter
Combining Self-Supervised and Supervised Learning with Noisy Labels
Yongqi Zhang, Hui Zhang, Quanming Yao +1
Since convolutional neural networks (CNNs) can easily overfit noisy labels, which are ubiquitous in visual classification tasks, it has been a great challenge to train CNNs against…
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han, Quanming Yao, Tongliang Liu +4
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios. However…
Automorphic Equivalence-aware Graph Neural Network
Fengli Xu, Quanming Yao, Pan Hui +1
Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e.g., computational biologist and social network analysis. Howeve…
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Wei He, Quanming Yao, Chao Li +4
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed…
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Lei Chen
Negative sampling, which samples negative triplets from non-observed ones in knowledge graph (KG), is an essential step in KG embedding. Recently, generative adversarial network (G…
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
Yuhui Ding, Quanming Yao, Huan Zhao +1
In this paper, we propose a novel framework to automatically utilize task-dependent semantic information which is encoded in heterogeneous information networks (HINs). Specifically…
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Jingtao Ding, Yuhan Quan, Quanming Yao +2
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data. As two major concerns in negative sampling…
Simplifying Architecture Search for Graph Neural Network
Huan Zhao, Lanning Wei, Quanming Yao
Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural archit…
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion
Yaqing Wang, Quanming Yao, James T. Kwok
Matrix learning is at the core of many machine learning problems. A number of real-world applications such as collaborative filtering and text mining can be formulated as a low-ran…
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases
Yu Liu, Quanming Yao, Yong Li
With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a.k…
AutoSTR: Efficient Backbone Search for Scene Text Recognition
Hui Zhang, Quanming Yao, Mingkun Yang +2
Scene text recognition (STR) is very challenging due to the diversity of text instances and the complexity of scenes. The community has paid increasing attention to boost the perfo…