Jingchao Ni

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it
• Graph Learning — 29 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Model Architectures — 8 citations into it
• Generative Models — 6 citations into it

Whom Jingchao Ni trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ronald J. Williams
5. Max Welling
6. Alex Krizhevsky
7. Yann LeCun
8. Thomas Kipf
9. Ilya Sutskever
10. Ruslan Salakhutdinov

Papers from Jingchao Ni's perspective

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

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

Their papers

• 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