26 citations · 36 across the 6 of their papers we have counts for
4 papers · 1 filter
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective
Chenyang Xi, Bo Tang, Jiajun Shen +3
Offline reinforcement learning (RL) has increasingly become the focus of the artificial intelligent research due to its wide real-world applications where the collection of data ma…
Scalable Graph Neural Networks for Heterogeneous Graphs
Lingfan Yu, Jiajun Shen, Jinyang Li +1
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature s…
Revisiting Self-Training for Neural Sequence Generation
Junxian He, Jiatao Gu, Jiajun Shen +1
Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's predi…
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen +4
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial appl…