Eric Battenberg

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 12 papers; trusts 115 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 179 citations into it; #4,631 of 35,345 by trust here, #2,006 by citations
• Signal Processing — 104 citations into it; #1,001 of 7,572 by trust here, #161 by citations
• Speech & Audio — 99 citations into it; #262 of 1,076 by trust here, #69 by citations
• Natural Language Processing — 67 citations into it; #3,437 of 5,688 by trust here, #407 by citations
• Computer Vision and Pattern Recognition — 54 citations into it; #16,718 of 21,575 by trust here, #8,831 by citations
• Generative Models — 48 citations into it; #1,793 of 1,956 by trust here, #501 by citations
• Model Architectures — 48 citations into it
• Optimization & Training — 18 citations into it

Whom Eric Battenberg trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Diederik P. Kingma
4. Yoshua Bengio
5. Dzmitry Bahdanau
6. Danilo Jimenez Rezende
7. Alex Krizhevsky
8. Ilya Sutskever
9. Yann LeCun
10. Jimmy Ba

Who trusts Eric Battenberg overall

1. Jinyu Li
2. Ye Jia
3. Wei-Ning Hsu
4. Xu Tan
5. Yu Zhang
6. Shan Yang
7. Dong Yu
8. Zhaoxi Mu
9. Yinjiao Lei
10. Aleksandr Laptev

Papers from Eric Battenberg's perspective

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

Their papers

• Exploring neural transducers for end-to-end speech recognition (2017) — 234 citations
• librosa: Audio and Music Signal Analysis in Python (2015) — 3,082 citations
• Wave-Tacotron: Spectrogram-Free End-to-End Text-to-Speech Synthesis (2020) — 111 citations
• Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis (2018) — 925 citations
• Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron (2018) — 611 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
• Uncovering Latent Style Factors for Expressive Speech Synthesis (2017) — 53 citations
• Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015) — 3,177 citations
• Lasagne: First release. (2015)
• Analyzing Drum Patterns Using Conditional Deep Belief Networks (2012) — 33 citations