Bo Zong

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 70 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it
• Graph Learning — 71 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it
• Statistical and Nonlinear Physics — 33 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Molecular Biology — 16 citations into it
• Generative Models — 12 citations into it
• Model Architectures — 11 citations into it

Whom Bo Zong trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Jimmy Ba
7. Ilya Sutskever
8. Max Welling
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Bo Zong overall

1. Niv Cohen
2. Liron Bergman
3. Jingkang Yang
4. Yedid Hoshen
5. Izhak Golan
6. Ailin Deng
7. Yunfu Song
8. Mengyu Wang
9. Shreshth Tuli
10. Kilian Batzner

Papers from Bo Zong's perspective

Ordered by how strongly the researchers Bo Zong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Substructure Assembling Network for Graph Classification (2018) — 28 citations
• Parameterized Explainer for Graph Neural Network (2020) — 824 citations
• Learning to Drop: Robust Graph Neural Network via Topological Denoising (2020) — 323 citations
• Robust Graph Representation Learning via Neural Sparsification (2020) — 338 citations
• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (2018) — 934 citations
• Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection (2018) — 2,127 citations