Navid Azizan

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 242 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 154 citations into it; #5,190 of 35,345 by trust here, #6,266 by citations
• Computer Vision and Pattern Recognition — 82 citations into it; #5,356 of 21,575 by trust here, #6,031 by citations
• Optimization & Training — 57 citations into it; #758 of 2,601 by trust here, #759 by citations
• Meta & Continual Learning — 40 citations into it; #210 of 1,041 by trust here, #263 by citations
• Computational Mechanics — 31 citations into it; #2,625 of 10,001 by trust here, #2,447 by citations
• Scaling & Learning Theory — 27 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Reinforcement Learning — 18 citations into it

Whom Navid Azizan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yann LeCun
5. Kaiming He
6. Yoshua Bengio
7. Alex Graves
8. Ilya Sutskever
9. Jimmy Ba
10. Radford M. Neal

Who trusts Navid Azizan overall

1. Youngjae Min
2. Namhoon Cho
3. Seyed Iman Mirzadeh
4. Mehrdad Farajtabar
5. Gobinda Saha
6. Ryan D'Orazio
7. Deepesh Data
8. Dong Yin
9. Yunhui Guo
10. Thang Van Doan

Papers from Navid Azizan's perspective

Ordered by how strongly the researchers Navid Azizan 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Orthogonal Gradient Descent for Continual Learning (2019) — 536 citations
• Improving Distributed Gradient Descent Using Reed-Solomon Codes (2017) — 166 citations
• Π-ORFit: One-Pass Learning with Bregman Projection (2024)
• SketchOGD: Memory-Efficient Continual Learning (2023)
• One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares (2022) — 24 citations
• Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks (2021) — 54 citations
• Stochastic Mirror Descent on Overparameterized Nonlinear Models (2019) — 83 citations
• Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization (2018) — 73 citations