Jonathan Shen

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 9 papers; trusts 270 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #2,929 of 35,345 by trust here, #1,050 by citations
• Speech & Audio — 102 citations into it; #179 of 1,076 by trust here, #35 by citations
• Signal Processing — 90 citations into it; #1,454 of 7,572 by trust here, #282 by citations
• Natural Language Processing — 66 citations into it; #1,893 of 5,688 by trust here, #427 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #15,252 of 21,575 by trust here, #18,782 by citations
• Model Architectures — 51 citations into it; #1,618 of 2,278 by trust here, #935 by citations
• Generative Models — 37 citations into it; #1,941 of 1,956 by trust here, #759 by citations
• Optimization & Training — 12 citations into it

Whom Jonathan Shen trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Graves
4. Yoshua Bengio
5. Alex Krizhevsky
6. Kaiming He
7. Yann LeCun
8. Karen Simonyan
9. Ilya Sutskever
10. Dzmitry Bahdanau

Who trusts Jonathan Shen overall

1. Ye Jia
2. Xu Tan
3. Yu Zhang
4. Yi Ren
5. Tao Qin
6. Heiga Zen
7. Michelle Tadmor Ramanovich
8. Cong Han
9. Lei He
10. Ron J. Weiss

Papers from Jonathan Shen's perspective

Ordered by how strongly the researchers Jonathan Shen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
• Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions (2017) — 3,120 citations
• Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (2018) — 963 citations
• Hierarchical Generative Modeling for Controllable Speech Synthesis (2018) — 295 citations
• In Teacher We Trust: Learning Compressed Models for Pedestrian Detection (2016) — 30 citations
• Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling (2020) — 116 citations
• Parallel Tacotron: Non-Autoregressive and Controllable TTS (2020) — 110 citations
• PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS (2021) — 100 citations
• Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling (2021) — 85 citations