Dongjin Song

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 7 papers; trusts 226 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #28,825 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Graph Learning — 31 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Natural Language Processing — 19 citations into it
• Model Architectures — 18 citations into it; #1,859 of 2,278 by trust here, #1,421 by citations
• Meta & Continual Learning — 17 citations into it

Whom Dongjin Song trusts overall

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

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

Who trusts Dongjin Song overall

1. Shiv Ram Dubey
2. Yao Qin
3. Weiwei Jiang
4. Slawek Smyl
5. Li Yuan
6. Chao Li
7. Yunhao Zhang
8. Xiao Wang
9. Tian Guo
10. Andrew Cook

Papers from Dongjin Song's perspective

Ordered by how strongly the researchers Dongjin Song 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Top Rank Supervised Binary Coding for Visual Search (2015) — 71 citations
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (2017) — 1,569 citations
• CGLB: Benchmark Tasks for Continual Graph Learning (2022) — 74 citations
• Robust Graph Representation Learning via Neural Sparsification (2020) — 338 citations
• Heterogeneous Graph Neural Network (2019) — 1,630 citations
• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (2018) — 934 citations
• Fast Structural Binary Coding (2016) — 22 citations