Gabriel Pereyra

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 4 papers; trusts 95 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #4,598 of 35,345 by trust here, #2,953 by citations
• Natural Language Processing — 73 citations into it
• Computer Vision and Pattern Recognition — 53 citations into it; #14,488 of 21,575 by trust here, #6,485 by citations
• Evaluation & Benchmarks — 28 citations into it
• Optimization & Training — 27 citations into it; #1,068 of 2,601 by trust here, #469 by citations
• Model Architectures — 26 citations into it; #1,654 of 2,278 by trust here, #1,421 by citations
• Signal Processing — 14 citations into it
• Efficiency & Compression — 9 citations into it; #816 of 1,264 by trust here, #504 by citations

Whom Gabriel Pereyra trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Christian Szegedy
5. Alex Graves
6. Dzmitry Bahdanau
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Sergey Ioffe
10. Yann LeCun

Who trusts Gabriel Pereyra overall

1. Yoshua Bengio
2. John Wieting
3. Mirco Ravanelli
4. Shiyu Liang
5. Kaiyang Zhou
6. Kimin Lee
7. Bo Liu
8. Quoc V. Le
9. Li Yuan
10. Graham Neubig

Papers from Gabriel Pereyra's perspective

Ordered by how strongly the researchers Gabriel Pereyra 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. 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. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Regularizing Neural Networks by Penalizing Confident Output Distributions (2017) — 1,268 citations
• Batch normalized recurrent neural networks (2015) — 224 citations
• Large scale distributed neural network training through online distillation (2018) — 455 citations
• OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization (2022) — 307 citations