Aaron Zweig

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 79 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #10,075 of 35,345 by trust here, #13,962 by citations
• Graph Learning — 39 citations into it; #244 of 693 by trust here, #429 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Generative Models — 15 citations into it; #719 of 1,956 by trust here, #1,271 by citations
• Statistical and Nonlinear Physics — 14 citations into it; #2,355 of 10,127 by trust here, #3,382 by citations
• Molecular Biology — 12 citations into it; #3,313 of 17,773 by trust here, #10,506 by citations
• Computational Theory and Mathematics — 7 citations into it

Whom Aaron Zweig trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Max Welling
5. Ronald J. Williams
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Ruslan Salakhutdinov
10. Tomas Mikolov

Who trusts Aaron Zweig overall

1. William L. Hamilton
2. Jiaxuan You
3. Hao Wang
4. Arash Vahdat
5. Jonathan Shlomi
6. Megha Nawhal
7. Wengong Jin
8. Davide Bacciu
9. Swarnadeep Saha
10. Rex Ying

Papers from Aaron Zweig's perspective

Ordered by how strongly the researchers Aaron Zweig 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Graphite: Iterative Generative Modeling of Graphs (2018) — 331 citations
• Stochastic Optimization of Sorting Networks via Continuous Relaxations (2019) — 220 citations