Matthew Wiesner

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 130 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #12,940 of 35,345 by trust here, #7,062 by citations
• Speech & Audio — 75 citations into it; #443 of 1,076 by trust here, #279 by citations
• Natural Language Processing — 73 citations into it; #4,278 of 5,688 by trust here, #3,529 by citations
• Signal Processing — 68 citations into it; #4,463 of 7,572 by trust here, #981 by citations
• Model Architectures — 32 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Matthew Wiesner trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jan Chorowski
5. Shinji Watanabe
6. Dzmitry Bahdanau
7. Ilya Sutskever
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Navdeep Jaitly

Who trusts Matthew Wiesner overall

1. Changhan Wang
2. Jinyu Li
3. Murali Karthick Baskar
4. Shinji Watanabe
5. Juan Pino
6. Adithya Renduchintala
7. Takaaki Hori
8. Steven Y. Feng
9. Tomoki Hayashi
10. Sameer Khurana

Papers from Matthew Wiesner's perspective

Ordered by how strongly the researchers Matthew Wiesner 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• ESPnet: End-to-End Speech Processing Toolkit (2018) — 1,730 citations
• Multilingual Sequence-to-Sequence Speech Recognition: Architecture, Transfer Learning, and Language Modeling (2018) — 130 citations
• Multi-Modal Data Augmentation for End-to-end ASR (2018) — 40 citations
• The Multilingual TEDx Corpus for Speech Recognition and Translation (2021) — 154 citations
• Massively Multilingual Adversarial Speech Recognition (2019) — 83 citations
• Low Resource Multi-modal Data Augmentation for End-to-end ASR (2018) — 29 citations
• Pretraining by Backtranslation for End-to-End ASR in Low-Resource Settings (2018) — 7 citations