Xiangang Li

Artificial Intelligence · with Wang · 2014–2021

A researcher in the Science corpus: 11 papers; trusts 222 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 257 citations into it; #9,504 of 35,345 by trust here, #6,569 by citations
• Signal Processing — 160 citations into it; #1,809 of 7,572 by trust here, #748 by citations
• Speech & Audio — 134 citations into it; #733 of 1,076 by trust here, #383 by citations
• Natural Language Processing — 102 citations into it
• Model Architectures — 68 citations into it
• Computer Vision and Pattern Recognition — 42 citations into it
• Optimization & Training — 27 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #3,056 of 3,145 by trust here, #1,671 by citations

Whom Xiangang Li trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Hasim Sak
6. Sepp Hochreiter
7. Razvan Pascanu
8. Tomas Mikolov
9. Andrew W. Senior
10. Abdel-rahman Mohamed

Who trusts Xiangang Li overall

1. Yajie Miao
2. Terrance Devries
3. Florian Metze
4. Graham W. Taylor
5. Xihong Wu
6. Albert Zeyer
7. Dong Yu
8. Dongwei Jiang
9. Gaëtan Hadjeres

Papers from Xiangang Li's perspective

Ordered by how strongly the researchers Xiangang Li 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio (2021) — 607 citations
• Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition (2014) — 324 citations
• Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling (2017) — 56 citations
• Deep Speaker: an End-to-End Neural Speaker Embedding System (2017) — 533 citations
• A Further Study of Unsupervised Pretraining for Transformer Based Speech Recognition (2020) — 32 citations
• Improving Transformer-based Speech Recognition Using Unsupervised Pre-training (2019) — 106 citations
• Didispeech: A Large Scale Mandarin Speech Corpus (2020) — 63 citations
• Comparable Study Of Modeling Units For End-To-End Mandarin Speech Recognition (2018) — 35 citations
• Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015) — 3,177 citations
• Modeling speaker variability using long short-term memory networks for speech recognition (2015) — 25 citations
• Chinese syllable-to-character conversion with recurrent neural network based supervised sequence labelling (2015) — 6 citations