Jie Yin

Artificial Intelligence · with Zhang · 2010–2020

A researcher in the Science corpus: 9 papers; trusts 139 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it; #19,821 of 35,345 by trust here, #14,438 by citations
• Statistical and Nonlinear Physics — 69 citations into it; #6,369 of 10,127 by trust here, #3,735 by citations
• Representation & Self-Supervised Learning — 54 citations into it
• Graph Learning — 45 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it
• Natural Language Processing — 26 citations into it
• Molecular Biology — 22 citations into it; #17,359 of 17,773 by trust here, #12,008 by citations
• Information Systems — 18 citations into it

Whom Jie Yin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Mark Newman
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Tomas Mikolov
8. Lei Tang
9. Yann LeCun
10. Ruslan Salakhutdinov

Who trusts Jie Yin overall

1. Shirui Pan
2. Muhammad Imran
3. Carlos Castillo
4. Meng Fang
5. Ruiqi Hu

Papers from Jie Yin's perspective

Ordered by how strongly the researchers Jie Yin 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Network Representation Learning: A Survey (2017) — 693 citations
• Homophily, Structure, and Content Augmented Network Representation Learning (2016) — 104 citations
• Sequence Classification Restricted Boltzmann Machines With Gated Units (2020) — 8 citations
• User Profile Preserving Social Network Embedding (2017) — 100 citations
• Interpreting Shared Deep Learning Models via Explicable Boundary Trees (2017) — 6 citations
• Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract (2015) — 601 citations
• TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015) — 46 citations
• Transfer Learning across Networks for Collective Classification (2013) — 55 citations
• SMILE: A Similarity-Based Approach for Multiple Instance Learning (2010) — 14 citations