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cs.LG2024★ 1 cited
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Zhen Yang, Ming Ding, Tinglin Huang +5
Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processi…
cs.LG2023
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
Zhen Yang, Tinglin Huang, Ming Ding +5
In-Batch contrastive learning is a state-of-the-art self-supervised method that brings semantically-similar instances close while pushing dissimilar instances apart within a mini-b…
cs.LG2022
GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang +4
Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate s…