Matt Shannon

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 5 papers; trusts 69 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #12,833 of 35,345 by trust here, #10,207 by citations
• Speech & Audio — 37 citations into it
• Natural Language Processing — 31 citations into it; #3,362 of 5,688 by trust here, #4,032 by citations
• Signal Processing — 29 citations into it; #4,041 of 7,572 by trust here, #4,560 by citations
• Generative Models — 27 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Model Architectures — 16 citations into it; #1,378 of 2,278 by trust here, #1,749 by citations

Whom Matt Shannon trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Dzmitry Bahdanau
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Keiichi Tokuda
7. J. Urgen Schmidhuber
8. Sepp Hochreiter
9. Kyunghyun Cho
10. Heiga Zen

Who trusts Matt Shannon overall

1. Heiga Zen
2. Athira Aroon
3. Jinyu Li
4. Li Deng
5. Linhao Dong
6. Dong Yu
7. Hasim Sak
8. S. Dhonde
9. Benigno Uria
10. Chao Weng

Papers from Matt Shannon's perspective

Ordered by how strongly the researchers Matt Shannon 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. Long Short-Term Memory
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping (2017) — 136 citations
• Autoregressive Models for Statistical Parametric Speech Synthesis (2013) — 68 citations
• Location-Relative Attention Mechanisms for Robust Long-Form Speech Synthesis (2019) — 126 citations
• Semi-Supervised Generative Modeling for Controllable Speech Synthesis (2019) — 48 citations
• Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis (2019) — 48 citations