Alex Bewley
Computer Vision and Pattern Recognition · with Hawke · 2017–2018
A researcher in the Science corpus: 4 papers; trusts 229 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 69 citations into it; #8,131 of 21,575 by trust here, #10,154 by citations
- Artificial Intelligence — 47 citations into it; #19,382 of 35,345 by trust here, #9,968 by citations
- Reinforcement Learning — 28 citations into it; #2,094 of 2,242 by trust here, #1,253 by citations
- Generative Models — 16 citations into it
- Automotive Engineering — 10 citations into it; #2,598 of 3,081 by trust here, #1,048 by citations
- Model Architectures — 9 citations into it
Whom Alex Bewley trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Andrew Zisserman
- Kaiming He
- Jimmy Ba
Who trusts Alex Bewley overall
- Jindong Jiang
- Ardi Tampuu
- Adam R. Kosiorek
- Garrett Wilson
- Michal Uřičář
- Klaus Greff
- Hao Wang
- Sepehr Janghorbani
- Anfeng He
- Daniel Zoran
Papers from Alex Bewley's perspective
Ordered by how strongly the researchers Alex Bewley 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Learning to Drive from Simulation without Real World Labels (2018) — 118 citations
- Learning to Drive in a Day (2018) — 800 citations
- Hierarchical Attentive Recurrent Tracking (2017) — 63 citations
- Incremental Adversarial Domain Adaptation for Continually Changing Environments (2017) — 138 citations