François Chollet

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 391 researchers and is trusted by 7,930 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #172 of 35,345 by trust here, #1,110 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #243 of 21,575 by trust here, #924 by citations
• Model Architectures — 38 citations into it; #28 of 2,278 by trust here, #171 by citations
• Natural Language Processing — 37 citations into it; #2,926 of 5,688 by trust here, #1,300 by citations
• Evaluation & Benchmarks — 14 citations into it; #2,574 of 3,147 by trust here, #632 by citations
• Reinforcement Learning — 14 citations into it
• Computational Theory and Mathematics — 9 citations into it; #6,990 of 12,182 by trust here, #3,218 by citations
• Efficiency & Compression — 9 citations into it; #426 of 1,264 by trust here, #925 by citations

Whom François Chollet trusts overall

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

1. Christian Szegedy
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Richard S. Sutton
5. Ilya Sutskever
6. Sergey Ioffe
7. Yann LeCun
8. Yoshua Bengio
9. Josh Tenenbaum
10. Tomas Mikolov

Who trusts François Chollet overall

1. Liang-Chieh Chen
2. Quoc V. Le
3. Felix Stahlberg
4. Chunhua Shen
5. Mingxing Tan
6. Jiahui Yu
7. Yihui He
8. Chen Sun
9. Barret Zoph
10. Sachin Mehta

Papers from François Chollet's perspective

Ordered by how strongly the researchers François Chollet 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. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Xception: Deep Learning with Depthwise Separable Convolutions (2016) — 18,118 citations
• Tensor2Tensor for Neural Machine Translation (2018) — 553 citations
• Depthwise Separable Convolutions for Neural Machine Translation (2017) — 314 citations
• DeepMath - Deep Sequence Models for Premise Selection (2016) — 263 citations
• Information-theoretical label embeddings for large-scale image classification (2016) — 17 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• The Measure of Intelligence (2019) — 16 citations
• Keras: The Python Deep Learning library (2018) — 1,498 citations