research
∙
06/06/2023
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
In-Batch contrastive learning is a state-of-the-art self-supervised meth...
share
research
∙
04/10/2023
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Graph self-supervised learning (SSL), including contrastive and generati...
research
∙
08/16/2022
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
Knowledge graph (KG) embeddings have been a mainstream approach for reas...
research
∙
06/22/2022
GACT: Activation Compressed Training for General Architectures
Training large neural network (NN) models requires extensive memory reso...
research
∙
05/28/2022
Rethinking the Setting of Semi-supervised Learning on Graphs
We argue that the present setting of semisupervised learning on graphs m...
research
∙
05/22/2022
GraphMAE: Self-Supervised Masked Graph Autoencoders
Self-supervised learning (SSL) has been extensively explored in recent y...
research
∙
12/08/2021
Improving the Training of Graph Neural Networks with Consistency Regularization
Graph neural networks (GNNs) have achieved notable success in the semi-s...
research
∙
11/08/2021
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Adversarial attacks on graphs have posed a major threat to the robustnes...
research
∙
03/01/2021
CogDL: An Extensive Toolkit for Deep Learning on Graphs
Graph representation learning aims to learn low-dimensional node embeddi...
research
∙
05/19/2020
Controllable Multi-Interest Framework for Recommendation
Recently, neural networks have been widely used in e-commerce recommende...
research
∙
08/15/2019
Towards Knowledge-Based Recommender Dialog System
In this paper, we propose a novel end-to-end framework called KBRD, whic...
research
∙
05/05/2019
