Ron Banner

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 293 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #10,333 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 86 citations into it; #27,045 of 35,345 by trust here, #10,319 by citations
• Efficiency & Compression — 53 citations into it; #1,046 of 1,264 by trust here, #950 by citations
• Optimization & Training — 42 citations into it; #2,437 of 2,601 by trust here, #1,509 by citations
• Model Architectures — 20 citations into it
• Generative Models — 18 citations into it; #1,134 of 1,956 by trust here, #1,796 by citations
• Natural Language Processing — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Ron Banner trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Sergey Ioffe
7. Yann LeCun
8. X. Zhang
9. Jimmy Ba
10. Christian Szegedy

Who trusts Ron Banner overall

1. Markus Nagel
2. Yelysei Bondarenko
3. Chengliang Zhang
4. Zi-Mei Yao
5. Tijmen Blankevoort
6. Tailin Liang
7. Jianfei Chen
8. Peisong Wang
9. Marios Fournarakis
10. Yu Emma Wang

Papers from Ron Banner's perspective

Ordered by how strongly the researchers Ron Banner trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Norm matters: efficient and accurate normalization schemes in deep networks (2018) — 191 citations
• Scalable Methods for 8-bit Training of Neural Networks (2018) — 375 citations
• GAN Steerability without optimization (2020) — 60 citations
• Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming (2020) — 157 citations
• Accurate Post Training Quantization With Small Calibration Sets (2021) — 250 citations
• Loss aware post-training quantization (2019) — 206 citations
• Post training 4-bit quantization of convolutional networks for rapid-deployment (2018) — 768 citations