Rik Sarkar

Artificial Intelligence · 2011–2022

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #20,971 of 35,345 by trust here, #18,586 by citations
• Graph Learning — 35 citations into it
• Statistical and Nonlinear Physics — 34 citations into it; #5,463 of 10,127 by trust here, #4,137 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Computer Networks and Communications — 11 citations into it
• Molecular Biology — 11 citations into it
• Management Science and Operations Research — 10 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it

Whom Rik Sarkar trusts overall

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

1. Robert D. Kleinberg
2. Diederik P. Kingma
3. Tomas Mikolov
4. Bryan Perozzi
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Ilya Sutskever
8. Thomas Kipf
9. Jimmy Ba
10. Max Welling

Who trusts Rik Sarkar overall

1. Ines Chami
2. Rex Ying
3. Christopher R'e
4. Ivana Balazevic
5. Octavian-Eugen Ganea
6. Frederic Sala
7. Aaron Lou
8. Jure Leskovec
9. Xiyuan Wang
10. Yujun Yan

Papers from Rik Sarkar's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
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. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GEMSEC: Graph Embedding with Self Clustering (2018) — 365 citations
• Multi-scale Attributed Node Embedding (2019) — 1,111 citations
• The Shapley Value in Machine Learning (2022) — 361 citations
• Low Distortion Delaunay Embedding of Trees in Hyperbolic Plane (2011) — 310 citations