Hassan Ghasemzadeh

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 365 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 179 citations into it; #7,458 of 35,345 by trust here, #5,495 by citations
• Computer Vision and Pattern Recognition — 151 citations into it; #9,341 of 21,575 by trust here, #5,161 by citations
• Meta & Continual Learning — 62 citations into it; #200 of 1,041 by trust here, #188 by citations
• Optimization & Training — 59 citations into it
• Representation & Self-Supervised Learning — 43 citations into it
• Efficiency & Compression — 33 citations into it; #458 of 1,264 by trust here, #433 by citations
• Model Architectures — 30 citations into it
• Signal Processing — 16 citations into it

Whom Hassan Ghasemzadeh trusts overall

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

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

Who trusts Hassan Ghasemzadeh overall

1. Seyed Iman Mirzadeh
2. Mehrdad Farajtabar
3. Liyuan Wang
4. Thang Van Doan
5. Jaehong Yoon
6. Yogesh Kumar Balaji
7. Divyam Madaan
8. Xingxing Zhang
9. Linfeng Zhang

Papers from Hassan Ghasemzadeh's perspective

Ordered by how strongly the researchers Hassan Ghasemzadeh 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

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
• Understanding the Role of Training Regimes in Continual Learning (2020) — 278 citations
• Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges (2017) — 836 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