Hessam Bagherinezhad
Computer Vision and Pattern Recognition · 2015–2018
A researcher in the Science corpus: 5 papers; trusts 575 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 125 citations into it; #4,523 of 21,575 by trust here, #5,236 by citations
- Artificial Intelligence — 81 citations into it; #12,375 of 35,345 by trust here, #8,077 by citations
- Efficiency & Compression — 28 citations into it
- Model Architectures — 17 citations into it
- Representation & Self-Supervised Learning — 16 citations into it
- Optimization & Training — 14 citations into it
- Control and Systems Engineering — 11 citations into it
- Evaluation & Benchmarks — 10 citations into it; #3,051 of 3,147 by trust here, #1,966 by citations
Whom Hessam Bagherinezhad trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Christian Szegedy
- Ilya Sutskever
- Ross B. Girshick
- Karen Simonyan
- Diederik P. Kingma
- Yann LeCun
Who trusts Hessam Bagherinezhad overall
- Xiangyu Zhang
- Yihui He
- Zhiqiang Shen
- Bichen Wu
- Angus Dempster
- Linfeng Zhang
- Sachin Mehta
- Jianping Gou
- Wonpyo Park
- Huizi Mao
Papers from Hessam Bagherinezhad's perspective
Ordered by how strongly the researchers Hessam Bagherinezhad 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)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images (2015) — 157 citations
- Label Refinery: Improving ImageNet Classification through Label Progression (2018) — 201 citations
- LCNN: Lookup-Based Convolutional Neural Network (2016) — 96 citations
- Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects (2016) — 52 citations
- Who Let the Dogs Out? Modeling Dog Behavior from Visual Data (2018) — 63 citations