Carolina Parada

Artificial Intelligence · with Alvarez · 2014–2022

A researcher in the Science corpus: 9 papers; trusts 62 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #4,668 of 35,345 by trust here, #2,899 by citations
• Computer Vision and Pattern Recognition — 103 citations into it
• Natural Language Processing — 58 citations into it
• Signal Processing — 47 citations into it; #2,698 of 7,572 by trust here, #2,001 by citations
• Speech & Audio — 26 citations into it; #236 of 1,076 by trust here, #394 by citations
• Model Architectures — 19 citations into it; #907 of 2,278 by trust here, #794 by citations
• Efficiency & Compression — 17 citations into it; #1,090 of 1,264 by trust here, #1,160 by citations
• Optimization & Training — 9 citations into it

Whom Carolina Parada trusts overall

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

1. Tara N. Sainath
2. Geoffrey E. Hinton
3. Yann LeCun
4. Andrew W. Senior
5. Yoshua Bengio
6. Joseph Dean
7. Alex Graves
8. Navdeep Jaitly
9. Marc'Aurelio Ranzato
10. Guoguo Chen

Who trusts Carolina Parada overall

1. Tara N. Sainath
2. Rohit Prabhavalkar
3. Yundong Zhang
4. Pete Warden
5. Vikas Sindhwani
6. Alice Coucke
7. Oleg Rybakov
8. Yanmin Qian
9. Preetum Nakkiran
10. Raziel Alvarez

Papers from Carolina Parada's perspective

Ordered by how strongly the researchers Carolina Parada trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) — 3,376 citations
• RT-1: Robotics Transformer for Real-World Control at Scale (2022) — 2,545 citations
• Locally-connected and convolutional neural networks for small footprint speaker recognition (2015) — 102 citations
• Small-footprint keyword spotting using deep neural networks (2014) — 589 citations
• Personalized speech recognition on mobile devices (2016) — 201 citations
• Convolutional neural networks for small-footprint keyword spotting (2015) — 584 citations
• Feature Learning with Raw-Waveform CLDNNs for Voice Activity Detection (2016) — 126 citations
• Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks (2015) — 93 citations
• Compressing deep neural networks using a rank-constrained topology (2015) — 81 citations