Henryk Michalewski
Artificial Intelligence · 2018–2025
A researcher in the Science corpus: 17 papers; trusts 242 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 601 citations into it; #8,709 of 35,345 by trust here, #554 by citations
- Natural Language Processing — 250 citations into it
- Computer Vision and Pattern Recognition — 216 citations into it
- Large Language Models — 138 citations into it; #505 of 799 by trust here, #125 by citations
- Reinforcement Learning — 133 citations into it; #1,543 of 2,242 by trust here, #778 by citations
- Information Systems — 108 citations into it; #4,541 of 8,771 by trust here, #224 by citations
- Model Architectures — 70 citations into it
- Software — 48 citations into it
Whom Henryk Michalewski trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Ilya Sutskever
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Alex Graves
- Kaiming He
- Yann LeCun
- J. Urgen Schmidhuber
- Ruslan Salakhutdinov
Who trusts Henryk Michalewski overall
Papers from Henryk Michalewski's perspective
Ordered by how strongly the researchers Henryk Michalewski trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- PaLM: Scaling Language Modeling with Pathways (2022) — 8,214 citations
- Program Synthesis with Large Language Models (2021) — 4,124 citations
- Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) — 1,075 citations
- Solving Quantitative Reasoning Problems with Language Models (2022) — 1,845 citations
- Model-Based Reinforcement Learning for Atari (2019) — 995 citations
- Language Model Cascades (2022) — 119 citations
- Natural Language to Code Generation in Interactive Data Science Notebooks (2022) — 122 citations
- Reinforcement Learning of Theorem Proving (2018) — 161 citations
- Sparse is Enough in Scaling Transformers (2021) — 125 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021) — 32 citations
- Expert-augmented actor-critic for ViZDoom and Montezumas Revenge (2018) — 8 citations
- Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes (2018) — 37 citations
- Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023) — 3,674 citations
- Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution (2023) — 525 citations