João Felipe Santos

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #17,806 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #12,662 of 21,575 by trust here, #16,787 by citations
• Model Architectures — 37 citations into it
• Natural Language Processing — 26 citations into it; #2,168 of 5,688 by trust here, #1,331 by citations
• Speech & Audio — 20 citations into it; #464 of 1,076 by trust here, #250 by citations
• Optimization & Training — 14 citations into it
• Generative Models — 13 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom João Felipe Santos trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Sepp Hochreiter
6. J. Urgen Schmidhuber
7. Alex Krizhevsky
8. Sergey Ioffe
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts João Felipe Santos overall

1. Adrian La'ncucki
2. Hao-Wen Dong
3. Takuma Okamoto
4. Yu Zhang
5. Nantheera Anantrasirichai
6. Eliya Nachmani
7. Zhaoyu Liu
8. Wei Ping
9. Zhen Zeng
10. Xu Tan

Papers from João Felipe Santos's perspective

Ordered by how strongly the researchers João Felipe Santos 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. 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Music transcription modelling and composition using deep learning (2016) — 190 citations
• Deep Complex Networks (2017) — 1,046 citations
• Representation Mixing for TTS Synthesis (2018) — 46 citations
• Char2Wav: End-to-End Speech Synthesis (2017) — 460 citations