Sertan Girgin

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 12 papers; trusts 106 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 338 citations into it
• Reinforcement Learning — 212 citations into it
• Computer Vision and Pattern Recognition — 95 citations into it
• Control and Systems Engineering — 47 citations into it
• Optimization & Training — 42 citations into it
• Model Architectures — 36 citations into it
• Electrical and Electronic Engineering — 23 citations into it
• Management Science and Operations Research — 23 citations into it

Whom Sertan Girgin trusts overall

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

1. Diederik P. Kingma
2. Ronald J. Williams
3. Sergey Levine
4. Geoffrey E. Hinton
5. John Schulman
6. Richard S. Sutton
7. Yoshua Bengio
8. Alex Krizhevsky
9. Tuomas Haarnoja
10. Yann LeCun

Papers from Sertan Girgin's perspective

Ordered by how strongly the researchers Sertan Girgin 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation (2021) — 578 citations
• What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study (2020) — 283 citations
• Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
• Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision (2023) — 291 citations
• Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
• What Matters for Adversarial Imitation Learning? (2021) — 102 citations
• Hyperparameter Selection for Imitation Learning (2021) — 21 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Gemma 3 Technical Report (2025) — 1,621 citations
• Continuous Control with Action Quantization from Demonstrations (2021) — 33 citations
• RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning (2021) — 42 citations
• What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study (2021) — 249 citations