Erica Cooper

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 6 papers; trusts 69 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #13,951 of 35,345 by trust here, #12,387 by citations
• Signal Processing — 71 citations into it; #4,921 of 7,572 by trust here, #1,958 by citations
• Speech & Audio — 63 citations into it; #1,003 of 1,076 by trust here, #590 by citations
• Natural Language Processing — 17 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Generative Models — 10 citations into it

Whom Erica Cooper trusts overall

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

1. Aäron van den Oord
2. Joon Son Chung
3. Geoffrey E. Hinton
4. Arsha Nagrani
5. Simon King
6. Andrew Zisserman
7. Diederik P. Kingma
8. Yoshua Bengio
9. Alex Graves
10. David Snyder

Who trusts Erica Cooper overall

1. Xu Tan
2. Tao Qin
3. Edresson Casanova
4. Frank K. Soong
5. Tie-Yan Liu
6. Hieu-Thi Luong
7. Junichi Yamagishi
8. Zexin Cai
9. Zhaoxi Mu
10. Yibin Zheng

Papers from Erica Cooper's perspective

Ordered by how strongly the researchers Erica Cooper 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Zero-Shot Multi-Speaker Text-To-Speech with State-Of-The-Art Neural Speaker Embeddings (2019) — 204 citations
• Text-to-Speech Synthesis Using Found Data for Low-Resource Languages (2019) — 20 citations
• Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction (2020) — 21 citations
• Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS? (2020) — 25 citations
• Learning Disentangled Phone and Speaker Representations in a Semi-Supervised VQ-VAE Paradigm (2020) — 27 citations
• Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis (2020) — 3 citations