Seyed Iman Mirzadeh

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 9 papers; trusts 591 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 282 citations into it; #7,264 of 35,345 by trust here, #5,187 by citations
• Computer Vision and Pattern Recognition — 203 citations into it; #9,040 of 21,575 by trust here, #5,422 by citations
• Meta & Continual Learning — 109 citations into it; #174 of 1,041 by trust here, #138 by citations
• Optimization & Training — 93 citations into it
• Representation & Self-Supervised Learning — 70 citations into it
• Model Architectures — 57 citations into it
• Efficiency & Compression — 38 citations into it; #459 of 1,264 by trust here, #433 by citations
• Reinforcement Learning — 28 citations into it

Whom Seyed Iman Mirzadeh trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Arslan Chaudhry
4. Kaiming He
5. Yoshua Bengio
6. Diederik P. Kingma
7. Yann LeCun
8. Marc'Aurelio Ranzato
9. Ilya Sutskever
10. Ian J. Goodfellow

Who trusts Seyed Iman Mirzadeh overall

1. Liyuan Wang
2. Mehrdad Farajtabar
3. Thang Van Doan
4. Xingxing Zhang
5. Jaehong Yoon
6. Linfeng Zhang
7. Divyam Madaan
8. Jianping Gou
9. Yogesh Kumar Balaji
10. Jun Zhu

Papers from Seyed Iman Mirzadeh's perspective

Ordered by how strongly the researchers Seyed Iman Mirzadeh 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Improved Knowledge Distillation via Teacher Assistant (2019) — 1,428 citations
• Linear Mode Connectivity in Multitask and Continual Learning (2020) — 180 citations
• Dropout as an Implicit Gating Mechanism For Continual Learning (2020) — 47 citations
• Wide Neural Networks Forget Less Catastrophically (2021) — 88 citations
• Understanding the Role of Training Regimes in Continual Learning (2020) — 278 citations
• Efficient Continual Learning Ensembles in Neural Network Subspaces (2022) — 7 citations
• Architecture Matters in Continual Learning (2022) — 64 citations
• ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition (2020) — 11 citations
• Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher (2019) — 159 citations