Kaveh Hassani

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 109 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #7,368 of 35,345 by trust here, #14,718 by citations
• Graph Learning — 33 citations into it; #169 of 693 by trust here, #355 by citations
• Representation & Self-Supervised Learning — 19 citations into it; #921 of 3,145 by trust here, #1,843 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #5,472 of 21,575 by trust here, #10,010 by citations
• Statistical and Nonlinear Physics — 13 citations into it
• Molecular Biology — 9 citations into it
• Materials Chemistry — 7 citations into it

Whom Kaveh Hassani trusts overall

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

1. Geoffrey E. Hinton
2. Thomas Kipf
3. Yoshua Bengio
4. Max Welling
5. Diederik P. Kingma
6. Sergey Ioffe
7. Kaiming He
8. Alex Krizhevsky
9. Johannes Klicpera
10. Christian Szegedy

Who trusts Kaveh Hassani overall

1. Yixin Liu
2. Shantanu Thakoor
3. Erlin Pan
4. Shaked Brody
5. Yaochen Xie
6. Zongbo Han
7. Joshua Robinson
8. Hengrui Zhang
9. Jun Xia
10. Shirui Pan

Papers from Kaveh Hassani's perspective

Ordered by how strongly the researchers Kaveh Hassani trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Contrastive Multi-View Representation Learning on Graphs (2020) — 1,725 citations