Dong Wang
Artificial Intelligence · with Li · 2009–2019
A researcher in the Science corpus: 7 papers; trusts 233 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 121 citations into it; #9,350 of 35,345 by trust here, #9,641 by citations
- Signal Processing — 64 citations into it
- Natural Language Processing — 54 citations into it; #2,231 of 5,688 by trust here, #2,634 by citations
- Representation & Self-Supervised Learning — 41 citations into it; #1,802 of 3,145 by trust here, #1,811 by citations
- Computer Vision and Pattern Recognition — 40 citations into it
- Speech & Audio — 30 citations into it; #583 of 1,076 by trust here, #797 by citations
- Model Architectures — 17 citations into it
- Optimization & Training — 9 citations into it; #1,845 of 2,601 by trust here, #2,519 by citations
Whom Dong Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Tomas Mikolov
- Richard Socher
- Ilya Sutskever
- Alex Krizhevsky
- Ruslan Salakhutdinov
- Yann LeCun
- Alex Graves
- Diederik P. Kingma
Who trusts Dong Wang overall
- John Giorgi
- Barret Zoph
- Mikel Artetxe
- Pegah Ghahremani
- Gary D. Bader
- Viktor Hangya
- Deniz Yuret
- Gorka Labaka
- Vimal Manohar
- Yuanshun Yao
Papers from Dong Wang's perspective
Ordered by how strongly the researchers Dong Wang 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation (2015) — 431 citations
- Deep Speaker Feature Learning for Text-Independent Speaker Verification (2017) — 89 citations
- Transfer learning for speech and language processing (2015) — 236 citations
- CN-Celeb: A Challenging Chinese Speaker Recognition Dataset (2019) — 264 citations
- Memory visualization for gated recurrent neural networks in speech recognition (2016) — 56 citations
- Pruning deep neural networks by optimal brain damage (2014) — 28 citations
- In Proc. Interspeech (2009)