Eric Thibodeau-Laufer

Artificial Intelligence · 2013–2015

A researcher in the Science corpus: 2 papers; trusts 64 researchers and is trusted by 477 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #3,453 of 35,345 by trust here, #6,569 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #1,941 of 21,575 by trust here, #4,807 by citations
• Probabilistic & Bayesian Methods — 34 citations into it
• Generative Models — 25 citations into it; #349 of 1,956 by trust here, #529 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 12 citations into it
• Signal Processing — 11 citations into it
• Statistical and Nonlinear Physics — 11 citations into it

Whom Eric Thibodeau-Laufer trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Pascal Vincent
5. Yann LeCun
6. Ruslan Salakhutdinov
7. Diederik P. Kingma
8. Ilya Sutskever
9. Radford M. Neal
10. Xavier Glorot

Who trusts Eric Thibodeau-Laufer overall

1. Yoshua Bengio
2. Philip Bachman
3. Kyunghyun Cho
4. Jost Tobias Springenberg
5. Aäron van den Oord
6. Jiaming Song
7. Ian J. Goodfellow
8. Abhinav Kumar Gupta
9. Garrett Wilson
10. Sergey Levine

Papers from Eric Thibodeau-Laufer's perspective

Ordered by how strongly the researchers Eric Thibodeau-Laufer 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Generative Stochastic Networks Trainable by Backprop (2013) — 400 citations
• GSNs : Generative Stochastic Networks (2015) — 50 citations