Bryan D. He
Artificial Intelligence · 2016–2017
A researcher in the Science corpus: 3 papers; trusts 52 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 46 citations into it; #10,611 of 35,345 by trust here, #15,004 by citations
- Natural Language Processing — 28 citations into it
- Computer Vision and Pattern Recognition — 25 citations into it; #7,711 of 21,575 by trust here, #11,083 by citations
- Evaluation & Benchmarks — 15 citations into it
- Information Retrieval & Search — 12 citations into it
- Statistics and Probability — 11 citations into it
- Computer Science Applications — 9 citations into it
- Molecular Biology — 7 citations into it
Whom Bryan D. He trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Martin J. Wainwright
- Alex Krizhevsky
- Robert Tibshirani
- Yoshua Bengio
- Benjamin Roth
- Joel A. Tropp
- Ilya Sutskever
- Yann LeCun
- Kaiming He
Who trusts Bryan D. He overall
- Alexander J. Ratner
- Paroma Varma
- Christopher R'e
- Stephen H. Bach
- Braden Hancock
- Jieyu Zhang
- Arghya Pal
- Xinyan Zhao
- Eric Zhan
- Henry R. Ehrenberg
Papers from Bryan D. He's perspective
Ordered by how strongly the researchers Bryan D. He 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning the Structure of Generative Models without Labeled Data (2017) — 182 citations
- Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016) — 26 citations
- Inferring Generative Model Structure with Static Analysis (2017) — 61 citations