Gabriel Bender

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 467 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #17,300 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 140 citations into it; #10,833 of 21,575 by trust here, #6,426 by citations
• Model Architectures — 67 citations into it
• Efficiency & Compression — 43 citations into it; #645 of 1,264 by trust here, #827 by citations
• Optimization & Training — 39 citations into it
• Neural Architecture Search & AutoML — 28 citations into it; #87 of 283 by trust here, #28 by citations
• Electrical and Electronic Engineering — 10 citations into it
• Reinforcement Learning — 8 citations into it

Whom Gabriel Bender trusts overall

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

1. Barret Zoph
2. Quoc V. Le
3. Kaiming He
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Karen Simonyan
8. Ilya Sutskever
9. Diederik P. Kingma
10. Ronald J. Williams

Who trusts Gabriel Bender overall

1. Xiangxiang Chu
2. Changlin Li
3. Xuanyi Dong
4. Jiahui Yu
5. Hanxiao Liu
6. Arber Zela
7. Bo Zhang
8. Renqian Luo
9. Wujie Wen
10. Han Cai

Papers from Gabriel Bender's perspective

Ordered by how strongly the researchers Gabriel Bender 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• CondConv: Conditionally Parameterized Convolutions for Efficient Inference (2019) — 870 citations
• BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models (2020) — 335 citations
• Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS (2020) — 131 citations
• Neural Predictor for Neural Architecture Search (2019) — 238 citations
• Understanding and Simplifying One-Shot Architecture Search (2018) — 824 citations