Zhen Huang
Artificial Intelligence · with Hu · 2017–2019
A researcher in the Science corpus: 5 papers; trusts 268 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 145 citations into it; #16,826 of 35,345 by trust here, #7,310 by citations
- Natural Language Processing — 125 citations into it; #2,976 of 5,688 by trust here, #1,674 by citations
- Computer Vision and Pattern Recognition — 95 citations into it; #10,901 of 21,575 by trust here, #5,236 by citations
- Information Retrieval & Search — 27 citations into it
- Evaluation & Benchmarks — 22 citations into it
- Model Architectures — 22 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Optimization & Training — 6 citations into it
Whom Zhen Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Oriol Vinyals
- Sepp Hochreiter
- Kaiming He
- Yann LeCun
- Alex Graves
Who trusts Zhen Huang overall
Papers from Zhen Huang's perspective
Ordered by how strongly the researchers Zhen Huang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Reinforced Mnemonic Reader for Machine Reading Comprehension (2017) — 216 citations
- Read + Verify: Machine Reading Comprehension with Unanswerable Questions (2018) — 164 citations
- Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (2019) — 68 citations
- A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (2019) — 98 citations
- Attention-Guided Answer Distillation for Machine Reading Comprehension (2018) — 76 citations