Hrayr Harutyunyan

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #22,574 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Model Architectures — 10 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Signal Processing — 6 citations into it

Whom Hrayr Harutyunyan trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Sepp Hochreiter
6. Yoshua Bengio
7. J. Urgen Schmidhuber
8. Yann LeCun
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Hrayr Harutyunyan overall

1. Chengxi Zang
2. C. Yuhua
3. Silvio Amir
4. Dianbo Liu
5. Chun-Hao Kingsley Chang
6. Yao Ming
7. Pengfei Chen
8. Sanjay Purushotham
9. Danding Wang
10. Huan-Zhi Song

Papers from Hrayr Harutyunyan's perspective

Ordered by how strongly the researchers Hrayr Harutyunyan 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multitask learning and benchmarking with clinical time series data (2017) — 1,081 citations
• Improving Generalization by Controlling Label-Noise Information in Neural Network Weights (2020) — 61 citations
• MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (2019) — 1,157 citations