Eli Schwartz

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 631 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 192 citations into it; #19,818 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 159 citations into it; #32,270 of 35,345 by trust here, #10,319 by citations
• Representation & Self-Supervised Learning — 58 citations into it
• Meta & Continual Learning — 24 citations into it
• Model Architectures — 24 citations into it
• Radiology, Nuclear Medicine and Imaging — 24 citations into it
• Generative Models — 18 citations into it
• Evaluation & Benchmarks — 16 citations into it

Whom Eli Schwartz trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yann LeCun
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Eli Schwartz overall

1. Yu-Xiong Wang
2. Qi Fan
3. Eunbyung Park
4. Akihiro Nakamura
5. Claudio Michaelis
6. Deva Ramanan
7. Markus Bayer
8. Wei Zhuo
9. Martial Hebert
10. Dan Andrei Ganea

Papers from Eli Schwartz's perspective

Ordered by how strongly the researchers Eli Schwartz 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Delta-encoder: an effective sample synthesis method for few-shot object recognition (2018) — 387 citations
• RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection (2018) — 355 citations
• UNIQ (2018) — 43 citations
• Baby steps towards few-shot learning with multiple semantics (2019) — 116 citations
• NICE: Noise Injection and Clamping Estimation for Neural Network Quantization (2018) — 68 citations
• RepMet: Representative-based metric learning for classification and one-shot object detection (2018) — 39 citations