Lu Zhang

Artificial Intelligence · with Jin · 2014–2016

A researcher in the Science corpus: 7 papers; trusts 92 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #8,046 of 35,345 by trust here, #4,336 by citations
• Natural Language Processing — 98 citations into it; #1,981 of 5,688 by trust here, #1,316 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #15,188 of 21,575 by trust here, #9,563 by citations
• Model Architectures — 30 citations into it
• Representation & Self-Supervised Learning — 30 citations into it; #2,972 of 3,145 by trust here, #1,754 by citations
• Information Systems — 9 citations into it; #3,380 of 8,771 by trust here, #1,189 by citations
• Large Language Models — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Lu Zhang trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Richard Socher
5. Tomas Mikolov
6. Alex Krizhevsky
7. Yann LeCun
8. Ronan Collobert
9. Diederik P. Kingma
10. Alex Graves

Who trusts Lu Zhang overall

1. Lili Mou
2. Xi Victoria Lin
3. Qian Chen
4. Xiaodong Gu
5. Ge Li
6. Masatoshi Tsuchiya
7. Ke Wang
8. Aditya Kanade
9. Ryan Marcus
10. Hongyu Zhang

Papers from Lu Zhang's perspective

Ordered by how strongly the researchers Lu Zhang 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Natural Language Inference by Tree-Based Convolution and Heuristic Matching (2015) — 368 citations
• How Transferable are Neural Networks in NLP Applications? (2016) — 305 citations
• On End-to-End Program Generation from User Intention by Deep Neural Networks (2015) — 48 citations
• Convolutional Neural Networks over Tree Structures for Programming Language Processing (2014) — 843 citations
• Discriminative Neural Sentence Modeling by Tree-Based Convolution (2015) — 127 citations
• Distilling Word Embeddings: An Encoding Approach (2015) — 28 citations
• Building Program Vector Representations for Deep Learning (2014) — 158 citations