Shir Gur

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 645 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #9,856 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 73 citations into it; #6,509 of 21,575 by trust here, #10,010 by citations
• Safety, Robustness & Interpretability — 41 citations into it; #1,886 of 2,251 by trust here, #2,231 by citations
• Model Architectures — 21 citations into it
• Biophysics — 18 citations into it
• Natural Language Processing — 14 citations into it
• Multimodal Learning — 11 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Shir Gur trusts overall

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

1. Andrea Vedaldi
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Ruth C. Fong
6. Yoshua Bengio
7. Karen Simonyan
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Shir Gur overall

1. Hila Chefer
2. S. Gadre
3. Mathilde Caron
4. Sachit Menon
5. Xuhong Li
6. Zizhao Zhang
7. Lior Wolf
8. Patrick Schramowski
9. Fanglei Xue
10. Siyuan Li

Papers from Shir Gur's perspective

Ordered by how strongly the researchers Shir Gur 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers (2021) — 484 citations
• Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization (2020) — 43 citations
• Transformer Interpretability Beyond Attention Visualization (2020) — 1,103 citations