Babak Ehteshami Bejnordi
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 7 papers; trusts 784 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 217 citations into it; #9,142 of 21,575 by trust here, #1,439 by citations
- Artificial Intelligence — 179 citations into it; #14,045 of 35,345 by trust here, #1,914 by citations
- Model Architectures — 64 citations into it
- Efficiency & Compression — 48 citations into it
- Representation & Self-Supervised Learning — 25 citations into it; #1,794 of 3,145 by trust here, #1,479 by citations
- Optimization & Training — 24 citations into it
- Evaluation & Benchmarks — 12 citations into it
- Generative Models — 12 citations into it
Whom Babak Ehteshami Bejnordi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Sergey Ioffe
- X. Zhang
- Christian Szegedy
- Ross B. Girshick
Who trusts Babak Ehteshami Bejnordi overall
- Thomas Verelst
- Ali Ehteshami Bejnordi
- Hengduo Li
- Wei He
- Yun Liu
- Chetan L. Srinidhi
- Davide Abati
- Tinne Tuytelaars
- Md. Zahangir Alom
- Faisal Mahmood
Papers from Babak Ehteshami Bejnordi's perspective
Ordered by how strongly the researchers Babak Ehteshami Bejnordi 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
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- A survey on deep learning in medical image analysis (2017) — 13,846 citations
- Conditional Channel Gated Networks for Task-Aware Continual Learning (2020) — 208 citations
- Stain Specific Standardization of Whole-Slide Histopathological Images (2016) — 294 citations
- Batch-shaping for learning conditional channel gated networks (2019) — 84 citations
- FrameExit: Conditional Early Exiting for Efficient Video Recognition (2021) — 109 citations
- Batch-Shaped Channel Gated Networks (2019) — 9 citations
- Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge (2018) — 307 citations