Junbo Zhang

Artificial Intelligence · AAAI Conference on Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 11 papers; trusts 247 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #10,001 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 61 citations into it
• Transportation — 59 citations into it; #601 of 2,062 by trust here, #192 by citations
• Building and Construction — 54 citations into it; #90 of 2,909 by trust here, #175 by citations
• Model Architectures — 49 citations into it
• Signal Processing — 42 citations into it
• Optimization & Training — 30 citations into it
• Natural Language Processing — 22 citations into it

Whom Junbo Zhang trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Yu Zheng
8. Sergey Ioffe
9. Yoshua Bengio
10. Jimmy Ba

Who trusts Junbo Zhang overall

1. Senzhang Wang
2. Xiuwen Yi
3. Haitao Yuan
4. Yu Zheng
5. Yunbo Wang
6. Saeed Rahmani
7. Jiannong Cao
8. Shengna Guo
9. Philip S. Yu
10. Yuxuan Liang

Papers from Junbo Zhang's perspective

Ordered by how strongly the researchers Junbo Zhang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023) — 520 citations
• Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction (2022) — 306 citations
• Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning (2020) — 313 citations
• Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (2019) — 632 citations
• GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction (2018) — 527 citations
• Deep Distributed Fusion Network for Air Quality Prediction (2018) — 328 citations
• When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks (2018) — 357 citations
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017) — 492 citations
• ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data (2016) — 214 citations
• Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2016) — 2,444 citations
• DNN-based prediction model for spatio-temporal data (2016) — 743 citations