Dandelion Mané
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 6 papers; trusts 350 researchers and is trusted by 1,059 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 191 citations into it; #1,959 of 35,345 by trust here, #1,896 by citations
- Computer Vision and Pattern Recognition — 148 citations into it; #1,336 of 21,575 by trust here, #1,383 by citations
- Model Architectures — 50 citations into it
- Safety, Robustness & Interpretability — 45 citations into it; #1,300 of 2,251 by trust here, #208 by citations
- Reinforcement Learning — 42 citations into it
- Optimization & Training — 38 citations into it; #647 of 2,601 by trust here, #79 by citations
- Representation & Self-Supervised Learning — 36 citations into it; #1,451 of 3,145 by trust here, #723 by citations
- Management Science and Operations Research — 19 citations into it
Whom Dandelion Mané trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Christian Szegedy
- Kaiming He
- Yoshua Bengio
- Barret Zoph
- Ilya Sutskever
- Yann LeCun
- Quoc V. Le
- Diederik P. Kingma
Who trusts Dandelion Mané overall
- Ekin Dogus Cubuk
- Quoc V. Le
- Raphael Gontijo Lopes
- Xiangxiang Chu
- Barret Zoph
- Ting-Li Chen
- Nicholas Carlini
- Mingxing Tan
- Hugo Touvron
- Han-Jia Ye
Papers from Dandelion Mané's perspective
Ordered by how strongly the researchers Dandelion Mané trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- AutoAugment: Learning Augmentation Policies from Data (2018) — 1,986 citations
- AutoAugment: Learning Augmentation Strategies From Data (2019) — 2,652 citations
- Adversarial Patch (2017) — 1,317 citations
- Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow (2018) — 348 citations
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016) — 11,798 citations
- Concrete Problems in AI Safety (2016) — 3,295 citations