Luisa M. Zintgraf

Artificial Intelligence · 2016–2025

A researcher in the Science corpus: 11 papers; trusts 400 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #4,594 of 35,345 by trust here, #4,526 by citations
• Computer Vision and Pattern Recognition — 122 citations into it; #4,603 of 21,575 by trust here, #4,726 by citations
• Reinforcement Learning — 77 citations into it; #2,169 of 2,242 by trust here, #1,545 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Computational Theory and Mathematics — 38 citations into it
• Meta & Continual Learning — 37 citations into it; #637 of 1,041 by trust here, #347 by citations
• Model Architectures — 23 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it; #1,491 of 1,525 by trust here, #878 by citations

Whom Luisa M. Zintgraf trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Yoshua Bengio
5. Chelsea Finn
6. Alex Krizhevsky
7. Karen Simonyan
8. Yann LeCun
9. Ilya Sutskever
10. Pieter Abbeel

Who trusts Luisa M. Zintgraf overall

1. Annie Xie
2. Wojciech Samek
3. Chelsea Finn
4. Marco Ancona
5. Sergey Levine
6. Housam Khalifa Bashier Babiker
7. K. Müller
8. Pieter-Jan Kindermans
9. James Harrison
10. Yinpeng Dong

Papers from Luisa M. Zintgraf's perspective

Ordered by how strongly the researchers Luisa M. Zintgraf 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis (2017) — 746 citations
• Deep Variational Reinforcement Learning for POMDPs (2018) — 308 citations
• CAML: Fast Context Adaptation via Meta-Learning (2018) — 38 citations
• VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (2019) — 331 citations
• Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors (2021) — 50 citations
• ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition (2021) — 55 citations
• Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making (2018) — 54 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• A practical guide to multi-objective reinforcement learning and planning (2021) — 587 citations
• Fast Context Adaptation via Meta-Learning (2018) — 432 citations
• A New Method to Visualize Deep Neural Networks (2016) — 48 citations