Jon Gauthier

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 235 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #11,481 of 35,345 by trust here, #7,797 by citations
• Natural Language Processing — 74 citations into it; #2,370 of 5,688 by trust here, #2,031 by citations
• Computer Vision and Pattern Recognition — 36 citations into it
• Model Architectures — 23 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #1,812 of 3,145 by trust here, #2,624 by citations
• Cognitive Neuroscience — 11 citations into it
• Developmental and Educational Psychology — 10 citations into it

Whom Jon Gauthier trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ruslan Salakhutdinov
4. Ian J. Goodfellow
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Kaiming He
10. Tomas Mikolov

Who trusts Jon Gauthier overall

1. Ming-Yu Liu
2. Chuan Li
3. Harrison Edwards
4. Sebastian Nowozin
5. Ke Li
6. Takuhiro Kaneko
7. William Lotter
8. Huiyuan Yang
9. Jie Gui
10. Daniel Jiwoong Im

Papers from Jon Gauthier's perspective

Ordered by how strongly the researchers Jon Gauthier 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Fast Unified Model for Parsing and Sentence Understanding (2016) — 323 citations
• Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017) — 65 citations
• A Systematic Assessment of Syntactic Generalization in Neural Language Models (2020) — 275 citations
• Word learning and the acquisition of syntactic-semantic overhypotheses (2018) — 11 citations
• A Paradigm for Situated and Goal-Driven Language Learning (2016) — 32 citations
• Conditional generative adversarial nets for convolutional face generation (2015)