Publications

Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein (2022). Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR’2022.

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Guocheng Qian, Hasan Abed Al Kader Hammoud, Guohao Li, Ali Thabet, Bernard Ghanem (2021). ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning. NeurIPS’2021 Spotlight.

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Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun (2021). Training Graph Neural Networks with 1000 layers. ICML’2021.

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Guohao Li, Matthias Müller, Guocheng Qian, Itzel C. Delgadillo, Abdulellah Abualshour, Ali Thabet, Bernard Ghanem (2021). DeepGCNs: Making GCNs Go as Deep as CNNs. TPAMI’2021.

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Guocheng Qian, Abdulellah Abualshour, Guohao Li, Ali Thabet, Bernard Ghanem (2021). PU-GCN: Point Cloud Upsampling via Graph Convolutional Network. CVPR’2021.

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Guohao Li, Chenxin Xiong, Ali Thabet, Bernard Ghanem (2020). DeeperGCN: All You Need to Train Deeper GCNs. arXiv’2020.

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Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Thabet, Bernard Ghanem (2019). SGAS: Sequential Greedy Architecture Search. CVPR’2020.

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Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem (2019). DeepGCNs: Can GCNs Go as Deep as CNNs?. ICCV’2019 Oral.

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Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem (2018). OIL: Observational Imitation Learning. RSS’2019.

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