Jose M. R. Sotelo

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 55 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #18,971 of 35,345 by trust here, #4,797 by citations
• Natural Language Processing — 69 citations into it; #2,621 of 5,688 by trust here, #1,143 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #9,382 of 21,575 by trust here, #4,438 by citations
• Generative Models — 40 citations into it; #482 of 1,956 by trust here, #286 by citations
• Reinforcement Learning — 24 citations into it
• Speech & Audio — 23 citations into it; #466 of 1,076 by trust here, #250 by citations
• Model Architectures — 19 citations into it
• Optimization & Training — 8 citations into it

Whom Jose M. R. Sotelo trusts overall

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

1. Alex Graves
2. Yoshua Bengio
3. Dzmitry Bahdanau
4. Geoffrey E. Hinton
5. Kyunghyun Cho
6. Diederik P. Kingma
7. Ilya Sutskever
8. Alex Krizhevsky
9. Paul Taylor
10. Oriol Vinyals

Who trusts Jose M. R. Sotelo overall

1. Wei Ping
2. Eliya Nachmani
3. Takuma Okamoto
4. Zhaoyu Liu
5. Chris Donahue
6. Kainan Peng
7. Yaniv Taigman
8. Lei He
9. Xu Tan
10. Ron J. Weiss

Papers from Jose M. R. Sotelo's perspective

Ordered by how strongly the researchers Jose M. R. Sotelo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SampleRNN: An Unconditional End-to-End Neural Audio Generation Model (2016) — 630 citations
• A Deep Reinforcement Learning Chatbot (2017) — 245 citations
• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis (2019) — 1,145 citations
• The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017)
• Char2Wav: End-to-End Speech Synthesis (2017) — 460 citations