Serkan Cabi

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 12 papers; trusts 251 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 483 citations into it; #30,188 of 35,345 by trust here, #4,018 by citations
• Reinforcement Learning — 322 citations into it; #2,171 of 2,242 by trust here, #560 by citations
• Computer Vision and Pattern Recognition — 261 citations into it; #12,394 of 21,575 by trust here, #2,938 by citations
• Control and Systems Engineering — 118 citations into it
• Robotics & Control — 46 citations into it
• Optimization & Training — 39 citations into it
• Computational Theory and Mathematics — 36 citations into it
• Mechanical Engineering — 19 citations into it

Whom Serkan Cabi trusts overall

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

1. Sergey Levine
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Richard S. Sutton
6. Yoshua Bengio
7. Ilya Sutskever
8. Kaiming He
9. Jimmy Ba
10. Alex Graves

Who trusts Serkan Cabi overall

1. Sergey Levine
2. Danfei Xu
3. Justin Fu
4. Aviral Kumar
5. Ajay Mandlekar
6. Jing Yu Koh
7. George Tucker
8. Misha Denil
9. Cosmin Paduraru
10. Ziyun Wang

Papers from Serkan Cabi's perspective

Ordered by how strongly the researchers Serkan Cabi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Multimodal Few-Shot Learning with Frozen Language Models (2021) — 990 citations
• Reinforcement and Imitation Learning for Diverse Visuomotor Skills (2018) — 347 citations
• Programmable Agents (2017) — 43 citations
• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) — 160 citations
• Acme: A Research Framework for Distributed Reinforcement Learning (2020) — 248 citations
• Learning Awareness Models (2018) — 49 citations
• One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL (2018) — 26 citations
• A Framework for Data-Driven Robotics (2019) — 30 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Gemini Robotics: Bringing AI into the Physical World (2025) — 385 citations
• Flamingo: a Visual Language Model for Few-Shot Learning (2022) — 6,220 citations
• Task-Relevant Adversarial Imitation Learning (2019) — 65 citations