Alexandre de Brébisson

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 8 papers; trusts 172 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #10,130 of 35,345 by trust here, #4,205 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #7,193 of 21,575 by trust here, #8,612 by citations
• Natural Language Processing — 76 citations into it
• Reinforcement Learning — 24 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Model Architectures — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Optimization & Training — 18 citations into it; #2,496 of 2,601 by trust here, #2,215 by citations

Whom Alexandre de Brébisson trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. L. Eon Bottou
4. Ilya Sutskever
5. Yann LeCun
6. Alex Krizhevsky
7. Kaiming He
8. Karen Simonyan
9. Pascal Vincent
10. Alex Graves

Who trusts Alexandre de Brébisson overall

1. Christian Wachinger
2. Neslihan Bayramoglu
3. Ankit Singh Rawat
4. Sekitoshi Kanai
5. Pim Moeskops
6. Matthew Veres
7. Yong Kiam Tan
8. Geert J. S. Litjens
9. Fausto Milletarì
10. Kashif Rasul

Papers from Alexandre de Brébisson's perspective

Ordered by how strongly the researchers Alexandre de Brébisson 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Deep Reinforcement Learning Chatbot (2017) — 245 citations
• Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets (2014) — 50 citations
• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis (2019) — 1,145 citations
• Deep neural networks for anatomical brain segmentation (2015) — 332 citations
• An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family (2015) — 105 citations
• The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017)
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations
• Artificial Neural Networks Applied to Taxi Destination Prediction (2015) — 197 citations