Piotr Sta'nczyk

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 9 papers; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 208 citations into it
• Reinforcement Learning — 110 citations into it
• Evaluation & Benchmarks — 46 citations into it
• Natural Language Processing — 34 citations into it
• Hardware and Architecture — 14 citations into it
• ML Systems & Hardware — 10 citations into it
• Information Systems — 8 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Piotr Sta'nczyk trusts overall

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

1. John Schulman
2. Ronald J. Williams
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Richard S. Sutton
6. Volodymyr Mnih
7. Alex Krizhevsky
8. Yoshua Bengio
9. Sergey Levine
10. Kaiming He

Who trusts Piotr Sta'nczyk overall

1. Scott Fujimoto

Papers from Piotr Sta'nczyk's perspective

Ordered by how strongly the researchers Piotr Sta'nczyk 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. 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes (2023) — 576 citations
• What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study (2020) — 283 citations
• Launchpad: A Programming Model for Distributed Machine Learning Research (2021) — 25 citations
• Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
• Google Research Football: A Novel Reinforcement Learning Environment (2019) — 523 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
• 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