DAG-GNN: DAG structure learning with graph neural networks
- Yue Yu
- Jie Chen
- et al.
- 2019
- ICML 2019
Publications
This is our catalog of publications authored by IBM researchers, in collaboration with the global research community. It’s an ever-growing body of work that shows why IBM is one of the most important contributors to modern computing.
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30 results for Jie Chen
A sequential set generation method for predicting set-valued outputs
- Tian Gao
- Jie Chen
- et al.
- 2019
- AAAI 2019
Constrained generation of semantically valid graphs via regularizing variational autoencoders
- Tengfei Ma
- Jie Chen
- et al.
- 2018
- NeurIPS 2018
FastGCN: Fast learning with graph convolu-tional networks via importance sampling
- Jie Chen
- Tengfei Ma
- et al.
- 2018
- ICLR 2018
Revisiting random binning features: Fast convergence and strong parallelizability
- Lingfei Wu
- Ian En Hsu Yen
- et al.
- 2016
- KDD 2016
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
- Tengfei Ma
- Trong Nghia Hoang
- et al.
- 2023
- UAI 2023
Graph enhanced cross-domain text-to-SQL generation
- Siyu Huo
- Tengfei Ma
- et al.
- 2019
- TextGraphs/EMNLP-IJCNLP 2019
Learning low-complexity autoregressive models with limited time sequence data
- Fu Lin
- Jie Chen
- 2017
- ACC 2017
Efficient one-vs-one kernel ridge regression for speech recognition
- Jie Chen
- Lingfei Wu
- et al.
- 2016
- ICASSP 2016
Graph coarsening: from scientific computing to machine learning
- Jie Chen
- Yousef Saad
- et al.
- 2022
- SeMA Journal