Hadar Serviansky

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #10,235 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 36 citations into it
• Graph Learning — 35 citations into it; #238 of 693 by trust here, #339 by citations
• Model Architectures — 22 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Materials Chemistry — 12 citations into it; #2,098 of 11,513 by trust here, #7,472 by citations
• Representation & Self-Supervised Learning — 9 citations into it

Whom Hadar Serviansky trusts overall

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

1. Haggai Maron
2. Diederik P. Kingma
3. Max Welling
4. Charles Ruizhongtai Qi
5. Thomas Kipf
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Oriol Vinyals
9. Yann LeCun
10. Yoshua Bengio

Who trusts Hadar Serviansky overall

1. William L. Hamilton
2. Jiaxuan You
3. Vijay Prakash Dwivedi
4. Nimrod Segol
5. Cristian Bodnar
6. Christopher Morris
7. Johannes Klicpera
8. Uri Alon

Papers from Hadar Serviansky's perspective

Ordered by how strongly the researchers Hadar Serviansky 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. 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

• Provably Powerful Graph Networks (2019) — 683 citations
• Set2Graph: Learning Graphs From Sets (2020) — 37 citations