Nicolas Heess
Nicolas Heess publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering, on topics such as Reinforcement Learning in Robotics, Adversarial Robustness in Machine Learning and Explainable Artificial Intelligence (XAI).
- World rank
- #1,908 of 1,633,909 ranked researchers
- Rank in United States
- #846 of 358,040
- Works
- 78
- Citations
- 3k
- Citations per work
- 37.9
What does Nicolas Heess research?
Shares of their own output, by the field each of their topics belongs to. The grey slice is everything not listed.
- 1Artificial Intelligence60% of their works
- 2Computer Vision and Pattern Recognition11% of their works
- 3Control and Systems Engineering8% of their works
- 4Computer Science Applications3% of their works
- 5Statistical and Nonlinear Physics3% of their works
- 6Computational Theory and Mathematics3% of their works
Research topics
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Explainable Artificial Intelligence (XAI)
- Robot Manipulation and Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
- Bayesian Modeling and Causal Inference
Which keywords describe Nicolas Heess's research?
The keywords of their largest research topics: Deep Learning, Neural Networks, Reinforcement Learning, Robotics, Machine Learning, Semi-Supervised Learning, Adversarial Examples and Robustness.
- Causal Inference
- Unsupervised
- Gaussian Processes
- Anomaly Detection
- Robot Learning
- Interpretable Models
- Robustness
- Semi-Supervised Learning
- Robotics
- Neural Networks
- Deep Learning
- Reinforcement Learning
- Machine Learning
- Adversarial Examples
- Grasping
- Machine Learning Interpretability
- Active Learning
- Domain Adaptation
- Transfer Learning
- Bayesian Networks
Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most.
All 20 words, with their numbers
- Deep Learning325 topics
- Neural Networks253 topics
- Reinforcement Learning171 topic
- Robotics171 topic
- Machine Learning82 topics
- Semi-Supervised Learning62 topics
- Adversarial Examples51 topic
- Robustness51 topic
- Grasping41 topic
- Interpretable Models41 topic
- Machine Learning Interpretability41 topic
- Robot Learning41 topic
- Active Learning31 topic
- Anomaly Detection31 topic
- Domain Adaptation31 topic
- Gaussian Processes31 topic
- Transfer Learning31 topic
- Unsupervised31 topic
- Bayesian Networks21 topic
- Causal Inference21 topic
Where does Nicolas Heess work?
Nicolas Heess's main affiliation in the publication record is Google (United States), United States.
How many publications and citations does Nicolas Heess have?
Science Explorer counts 78 works and 2,953 citations for Nicolas Heess, ranking #1,908 of 1,633,909 researchers worldwide on the composite score.
Ranked on field-normalised excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.
Papers, co-authors, who cited this work and researchers on the nearest topics are in the interactive view on the map. Is this your page? Request a correction or removal.