Haifeng Chen

Artificial Intelligence · International Conference on Learning Representations · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 78 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #28,740 of 35,345 by trust here, #8,706 by citations
• Graph Learning — 55 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Statistical and Nonlinear Physics — 41 citations into it
• Computer Networks and Communications — 32 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Natural Language Processing — 27 citations into it
• Model Architectures — 19 citations into it; #1,861 of 2,278 by trust here, #1,421 by citations

Whom Haifeng Chen trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Jimmy Ba
8. Ronald J. Williams
9. Tomas Mikolov
10. Alex Graves

Who trusts Haifeng Chen overall

1. Weiwei Jiang
2. Slawek Smyl

Papers from Haifeng Chen's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
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

• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017) — 1,569 citations
• Parameterized Explainer for Graph Neural Network (2020) — 824 citations
• You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020) — 312 citations
• Learning to Drop: Robust Graph Neural Network via Topological Denoising (2020) — 323 citations
• Robust Graph Representation Learning via Neural Sparsification (2020) — 338 citations
• NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (2018) — 406 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