Hamid Eghbalzadeh

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it
• Reinforcement Learning — 60 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Model Architectures — 36 citations into it
• Optimization & Training — 13 citations into it

Whom Hamid Eghbalzadeh trusts overall

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

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

Papers from Hamid Eghbalzadeh's perspective

Ordered by how strongly the researchers Hamid Eghbalzadeh 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning (2022) — 19 citations
• Efficient Training of Audio Transformers with Patchout (2021) — 410 citations
• History Compression via Language Models in Reinforcement Learning (2022) — 53 citations