Ari Seff

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 6 papers; trusts 288 researchers and is trusted by 1,338 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 69 citations into it; #1,156 of 21,575 by trust here, #1,195 by citations
• Artificial Intelligence — 50 citations into it; #3,721 of 35,345 by trust here, #2,158 by citations
• Evaluation & Benchmarks — 18 citations into it; #463 of 3,147 by trust here, #345 by citations
• Model Architectures — 11 citations into it
• Optimization & Training — 11 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Generative Models — 7 citations into it; #676 of 1,956 by trust here, #961 by citations

Whom Ari Seff trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yoshua Bengio
7. Kaiming He
8. Antonio Torralba
9. Jimmy Ba
10. Karen Simonyan

Who trusts Ari Seff overall

1. Zixuan Ke
2. Bing Liu
3. Gyuhak Kim
4. Fei Ye
5. Lei Tai
6. Matthias De Lange
7. Hoang Thanh-Tung
8. Yiduo Guo
9. Timothée Lesort
10. Jun-Yan Zhu

Papers from Ari Seff's perspective

Ordered by how strongly the researchers Ari Seff 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop (2015) — 2,676 citations
• DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving (2015) — 1,868 citations
• Continual Learning in Generative Adversarial Nets (2017) — 135 citations
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015) — 585 citations
• Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop (2015)
• A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations (2014) — 580 citations