Oliver Watts

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 8 papers; trusts 89 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #6,671 of 35,345 by trust here, #8,007 by citations
• Natural Language Processing — 52 citations into it
• Signal Processing — 47 citations into it; #4,740 of 7,572 by trust here, #2,052 by citations
• Speech & Audio — 40 citations into it; #347 of 1,076 by trust here, #600 by citations
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Oliver Watts trusts overall

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

1. Heiga Zen
2. David M. Blei
3. Hinrich Schütze
4. Keiichi Tokuda
5. Budiman Minasny
6. Hideki Kawahara
7. Isabelle M Guyon
8. K. Shinoda
9. Alessandro Lenci
10. Geoffrey E. Hinton

Who trusts Oliver Watts overall

1. Shan Yang
2. Heng Lu
3. Zhizheng Wu
4. Simon King
5. Gustav Eje Henter
6. Igor Jauk
7. Haitong Zhang
8. Erica Cooper
9. Junichi Yamagishi
10. Guanghui Xu

Papers from Oliver Watts's perspective

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

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis (2015) — 270 citations
• Improving Speech Synthesis with Discourse Relations (2019) — 16 citations
• Merlin: An Open Source Neural Network Speech Synthesis System (2016) — 345 citations
• Thousands of Voices for HMM-Based Speech Synthesis–Analysis and Application of TTS Systems Built on Various ASR Corpora (2010) — 102 citations
• Sentence-level control vectors for deep neural network speech synthesis (2015) — 54 citations
• Median-based generation of synthetic speech durations using a non-parametric approach (2016) — 19 citations
• Combining a vector space representation of linguistic context with a deep neural network for text-to-speech synthesis (2013) — 64 citations
• Unsupervised learning for text-to-speech synthesis (2013) — 55 citations