Brian D. Ziebart
Artificial Intelligence · 2000–2010
A researcher in the Science corpus: 6 papers; trusts 46 researchers and is trusted by 799 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 32 citations into it; #644 of 35,345 by trust here, #7,471 by citations
- Reinforcement Learning — 30 citations into it; #53 of 2,242 by trust here, #149 by citations
- Control and Systems Engineering — 14 citations into it; #1,966 of 8,257 by trust here, #2,782 by citations
- Computer Vision and Pattern Recognition — 13 citations into it
- Computational Theory and Mathematics — 9 citations into it
- Cognitive Neuroscience — 7 citations into it
- Information Theory — 7 citations into it
- Management Science and Operations Research — 5 citations into it
Whom Brian D. Ziebart trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard Bellman
- Andrew Y. Ng
- E. Todorov
- Pieter Abbeel
- Marc Toussaint
- John D. Lafferty
- Kevin P. Murphy
- David F. Shanno
- Richard S. Sutton
- J. Bagnell
Who trusts Brian D. Ziebart overall
- Sergey Levine
- Pieter Abbeel
- Chelsea Finn
- Vladlen Koltun
- Justin Fu
- Anca Dragan
- Nicholas Rhinehart
- J. Bagnell
- Kris M. Kitani
- Yoshua Bengio
Papers from Brian D. Ziebart's perspective
Ordered by how strongly the researchers Brian D. Ziebart 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Reinforcement Learning: An Introduction
Their papers
- Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy (2010) — 770 citations
- Planning-based prediction for pedestrians (2009) — 544 citations
- Modeling Interaction via the Principle of Maximum Causal Entropy (2010) — 393 citations
- Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008) — 327 citations
- Maximum Entropy Inverse Reinforcement Learning (2008) — 3,476 citations
- Task assignment with unknown duration (2000) — 951 citations