Seyed Kamyar Seyed Ghasemipour

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 127 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #14,845 of 35,345 by trust here, #2,338 by citations
• Computer Vision and Pattern Recognition — 75 citations into it
• Reinforcement Learning — 58 citations into it; #1,163 of 2,242 by trust here, #1,443 by citations
• Generative Models — 53 citations into it
• Control and Systems Engineering — 17 citations into it; #4,891 of 8,257 by trust here, #4,262 by citations
• Statistical and Nonlinear Physics — 13 citations into it
• Management Science and Operations Research — 11 citations into it; #7,908 of 11,946 by trust here, #5,787 by citations
• Probabilistic & Bayesian Methods — 10 citations into it

Whom Seyed Kamyar Seyed Ghasemipour trusts overall

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

1. Sergey Levine
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Stefan Schaal
5. Pieter Abbeel
6. Diederik P. Kingma
7. Jan Peters
8. Justin Fu
9. Yoshua Bengio
10. E. Todorov

Who trusts Seyed Kamyar Seyed Ghasemipour overall

1. Robert Dadashi
2. L'eonard Hussenot
3. Scott Fujimoto
4. Minghuan Liu
5. Ilya Kostrikov
6. Sergey Levine
7. Anurag Ajay
8. Rasool Fakoor
9. Charles Burton Snell
10. Aviral Kumar

Papers from Seyed Kamyar Seyed Ghasemipour's perspective

Ordered by how strongly the researchers Seyed Kamyar Seyed Ghasemipour 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
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

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (2022) — 8,628 citations
• EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL (2020) — 146 citations
• A Divergence Minimization Perspective on Imitation Learning Methods (2019) — 292 citations
• Gradient-based Optimization of Neural Network Architecture (2018) — 12 citations