Bin Gao
Artificial Intelligence · with Liu · 2014–2015
A researcher in the Science corpus: 5 papers; trusts 55 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #10,883 of 35,345 by trust here, #8,365 by citations
- Natural Language Processing — 61 citations into it
- Representation & Self-Supervised Learning — 42 citations into it; #1,628 of 3,145 by trust here, #1,430 by citations
- Computer Vision and Pattern Recognition — 13 citations into it
- Graph Learning — 12 citations into it; #326 of 693 by trust here, #298 by citations
- Large Language Models — 11 citations into it
- Optimization & Training — 6 citations into it
Whom Bin Gao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Tomas Mikolov
- L. Eon Bottou
- Richard Socher
- Ronan Collobert
- Jason Aaron Edward Weston
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
Who trusts Bin Gao overall
- Shaosheng Cao
- Nikola Mrksic
- Saket Navlakha
- Vincent Wenchen Zheng
- Hongyun Cai
- Gintare Karolina Dziugaite
- Sascha Rothe
- Piotr Wojciech Mirowski
- Peng Cui
- Shirui Pan
Papers from Bin Gao's perspective
Ordered by how strongly the researchers Bin Gao trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Learning Deep Representations for Graph Clustering (2014) — 684 citations
- RC-NET: A General Framework for Incorporating Knowledge into Word Representations (2014) — 205 citations
- Knowledge-Powered Deep Learning for Word Embedding (2014) — 188 citations
- KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge (2015) — 18 citations
- Co-learning of Word Representations and Morpheme Representations (2014) — 98 citations