Jia Cui
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 8 papers; trusts 91 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 141 citations into it; #19,342 of 35,345 by trust here, #10,942 by citations
- Signal Processing — 77 citations into it; #6,357 of 7,572 by trust here, #3,566 by citations
- Natural Language Processing — 66 citations into it
- Speech & Audio — 55 citations into it; #904 of 1,076 by trust here, #738 by citations
- Model Architectures — 33 citations into it; #2,068 of 2,278 by trust here, #2,215 by citations
- Computer Vision and Pattern Recognition — 31 citations into it
- Optimization & Training — 21 citations into it; #2,546 of 2,601 by trust here, #2,428 by citations
Whom Jia Cui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Graves
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yann LeCun
- James Martens
- Jimmy Ba
- Dzmitry Bahdanau
- Alex Krizhevsky
- Ilya Sutskever
Who trusts Jia Cui overall
- Jinyu Li
- Peidong Wang
- Chao Weng
- Jianping Gou
- Shahram Ghorbani
- Dong Yu
- Mark John Francis Gales
- Rohit Prabhavalkar
- Tara N. Sainath
- Gakuto Kurata
Papers from Jia Cui's perspective
Ordered by how strongly the researchers Jia Cui 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)
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition (2019) — 40 citations
- Multilingual representations for low resource speech recognition and keyword search (2015) — 103 citations
- Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition (2018) — 59 citations
- Improving Attention-Based End-to-End ASR Systems with Sequence-Based Loss Functions (2018) — 10 citations
- Knowledge distillation across ensembles of multilingual models for low-resource languages (2017) — 61 citations
- Token-wise Training for Attention Based End-to-end Speech Recognition (2019) — 5 citations
- Large Margin Training for Attention Based End-to-End Speech Recognition (2019) — 8 citations
- Efficient Knowledge Distillation from an Ensemble of Teachers (2017) — 280 citations