Rafael Valle

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 111 researchers and is trusted by 163 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #1,735 of 35,345 by trust here, #3,508 by citations
• Signal Processing — 41 citations into it; #180 of 7,572 by trust here, #316 by citations
• Speech & Audio — 40 citations into it; #55 of 1,076 by trust here, #159 by citations
• Natural Language Processing — 33 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Generative Models — 26 citations into it; #150 of 1,956 by trust here, #333 by citations
• Statistics and Probability — 13 citations into it; #9,179 of 12,932 by trust here, #12,077 by citations
• Model Architectures — 12 citations into it

Whom Rafael Valle trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Laurent Dinh
5. Jimmy Ba
6. Yuxuan Wang
7. Alex Graves
8. Alex Krizhevsky
9. Ryan J. Prenger
10. Ilya Sutskever

Who trusts Rafael Valle overall

1. Jaehyeon Kim
2. Xu Tan
3. Adrian La'ncucki
4. Kevin J. Shih
5. Wei Ping
6. Zhaoxi Mu
7. Paarth Neekhara
8. Jungil Kong
9. D. Lim
10. Kurniawati Azizah

Papers from Rafael Valle's perspective

Ordered by how strongly the researchers Rafael Valle 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Waveglow: A Flow-based Generative Network for Speech Synthesis (2018) — 1,161 citations
• Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis (2020) — 135 citations
• Missing Data Imputation for Supervised Learning (2016) — 74 citations
• One TTS Alignment to Rule Them All (2021) — 106 citations
• RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis (2021)
• Mellotron: Multispeaker Expressive Voice Synthesis by Conditioning on Rhythm, Pitch and Global Style Tokens (2019) — 165 citations