Yu Hu

Natural Language Processing · with Jiang · 2012–2016

A researcher in the Science corpus: 6 papers; trusts 38 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 57 citations into it; #5,289 of 5,688 by trust here, #3,977 by citations
• Artificial Intelligence — 56 citations into it; #15,732 of 35,345 by trust here, #14,181 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #2,924 of 3,145 by trust here, #2,587 by citations
• Evaluation & Benchmarks — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it; #21,051 of 21,575 by trust here, #20,271 by citations
• Signal Processing — 8 citations into it; #4,763 of 7,572 by trust here, #5,086 by citations
• Large Language Models — 7 citations into it
• Model Architectures — 7 citations into it

Whom Yu Hu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Tomas Mikolov
5. Ronan Collobert
6. Alex Krizhevsky
7. Yann LeCun
8. Dong Yu
9. Jason Aaron Edward Weston
10. Diederik P. Kingma

Who trusts Yu Hu overall

1. Hui Jiang
2. QUAN LIU
3. Ossama Abdel-Hamid
4. Ali Emami
5. Shiliang Zhang

Papers from Yu Hu's perspective

Ordered by how strongly the researchers Yu Hu 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition: Why DNN surpasses GMMS in acoustic modeling (2012) — 103 citations
• Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints (2015) — 141 citations
• Probabilistic Reasoning via Deep Learning: Neural Association Models (2016) — 103 citations
• Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge (2016) — 15 citations
• Combing Context and Commonsense Knowledge Through Neural Networks for Solving Winograd Schema Problems (2016) — 35 citations
• Investigation on dimensionality reduction of concatenated features with deep neural network for LVCSR systems (2012) — 25 citations