Jindong Jiang

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 295 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 70 citations into it; #9,739 of 21,575 by trust here, #11,950 by citations
• Artificial Intelligence — 63 citations into it
• Generative Models — 33 citations into it; #1,335 of 1,956 by trust here, #1,681 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Reinforcement Learning — 14 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 6 citations into it

Whom Jindong Jiang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Danilo Jimenez Rezende
4. Ronald J. Williams
5. Yoshua Bengio
6. Alex Krizhevsky
7. Jimmy Ba
8. Ruslan Salakhutdinov
9. J. Urgen Schmidhuber
10. Yann LeCun

Who trusts Jindong Jiang overall

1. Klaus Greff
2. Sjoerd van Steenkiste
3. Eric Crawford
4. J. Urgen Schmidhuber
5. Aleksandar Stani'c
6. Francesco Locatello
7. Joelle Pineau
8. Yang Liu
9. Dirk Weissenborn
10. Matthew James Vowels

Papers from Jindong Jiang's perspective

Ordered by how strongly the researchers Jindong Jiang 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition (2020) — 280 citations
• SCALOR: Generative World Models with Scalable Object Representations (2019) — 154 citations
• Generative Neurosymbolic Machines (2020) — 73 citations
• Scalable Object-Oriented Sequential Generative Models (2019) — 16 citations