Etai Littwin

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 4 papers; trusts 482 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #20,285 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #12,644 of 21,575 by trust here, #15,529 by citations
• Model Architectures — 31 citations into it
• Optimization & Training — 23 citations into it
• Scaling & Learning Theory — 17 citations into it
• Signal Processing — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #2,038 of 3,145 by trust here, #2,805 by citations
• Natural Language Processing — 13 citations into it

Whom Etai Littwin trusts overall

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

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

Who trusts Etai Littwin overall

1. Gabi Shalev
2. Bharath Hariharan
3. Yossi Adi
4. Ross B. Girshick
5. Xi Yin
6. Joseph Keshet
7. Hessam Bagherinezhad

Papers from Etai Littwin's perspective

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

Their papers

• The Multiverse Loss for Robust Transfer Learning (2015) — 24 citations
• Stabilizing Transformer Training by Preventing Attention Entropy Collapse (2023) — 184 citations
• Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics (2021) — 81 citations
• On the Convex Behavior of Deep Neural Networks in Relation to the Layers' Width (2019) — 3 citations