Zhengping Che

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 11 papers; trusts 273 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #11,197 of 35,345 by trust here, #5,084 by citations
• Computer Vision and Pattern Recognition — 117 citations into it
• Model Architectures — 53 citations into it; #1,780 of 2,278 by trust here, #1,048 by citations
• Representation & Self-Supervised Learning — 53 citations into it
• Natural Language Processing — 37 citations into it
• Signal Processing — 36 citations into it; #2,539 of 7,572 by trust here, #612 by citations
• Evaluation & Benchmarks — 34 citations into it
• Optimization & Training — 34 citations into it

Whom Zhengping Che trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Jerome H. Friedman
6. Ilya Sutskever
7. Yann LeCun
8. Pascal Vincent
9. Ruslan Salakhutdinov
10. Jimmy Ba

Who trusts Zhengping Che overall

1. Zachary Chase Lipton
2. Edward Choi
3. Cao Xiao
4. Joseph D. Futoma
5. Alfredo Vellido
6. David C. Kale
7. Yuan Luo
8. Sanjay Purushotham
9. Yinchong Yang
10. Sarah Tan

Papers from Zhengping Che's perspective

Ordered by how strongly the researchers Zhengping Che 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Recurrent Neural Networks for Multivariate Time Series with Missing Values (2016) — 2,465 citations
• Deep Computational Phenotyping (2015) — 267 citations
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control (2020) — 56 citations
• Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning (2021) — 13 citations
• D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios (2019) — 89 citations
• Benchmarking deep learning models on large healthcare datasets (2018) — 399 citations
• Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets (2017) — 85 citations
• Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records (2017) — 180 citations
• Interpretable Deep Models for ICU Outcome Prediction (2016) — 293 citations
• Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015) — 147 citations
• Causal Phenotype Discovery via Deep Networks (2015) — 36 citations