C. Karen Liu
Artificial Intelligence · with Turk · 2014–2022
A researcher in the Science corpus: 8 papers; trusts 259 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 124 citations into it; #15,729 of 35,345 by trust here, #11,750 by citations
- Computer Vision and Pattern Recognition — 124 citations into it
- Reinforcement Learning — 94 citations into it; #1,238 of 2,242 by trust here, #1,300 by citations
- Control and Systems Engineering — 88 citations into it; #1,930 of 8,257 by trust here, #644 by citations
- Robotics & Control — 39 citations into it
- Biomedical Engineering — 21 citations into it; #9,683 of 10,734 by trust here, #8,749 by citations
- Generative Models — 13 citations into it
- Optimization & Training — 10 citations into it
Whom C. Karen Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sergey Levine
- Pieter Abbeel
- John Schulman
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Richard S. Sutton
- Jonathan Ho
- Andrew Y. Ng
Who trusts C. Karen Liu overall
- OpenAI
- Priyanka Mandikal
- Xue Bin Peng
- Tao Chen
- Sammy Joe Christen
- Lerrel Pinto
- Shaojun Zhu
- Jie Tan
- Marcin Andrychowicz
- Herke Van Hoof
Papers from C. Karen Liu's perspective
Ordered by how strongly the researchers C. Karen Liu 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
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Preparing for the Unknown: Learning a Universal Policy with Online System Identification (2017) — 368 citations
- BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments (2021) — 255 citations
- DART: Dynamic Animation and Robotics Toolkit (2018) — 306 citations
- Learning symmetric and low-energy locomotion (2018) — 223 citations
- EDGE: Editable Dance Generation From Music (2022) — 404 citations
- BEHAVIOR-1K: A Benchmark for Embodied AI with 1, 000 Everyday Activities and Realistic Simulation (2022) — 370 citations
- Deep Haptic Model Predictive Control for Robot-Assisted Dressing (2017) — 93 citations
- Dexterous manipulation using both palm and fingers (2014) — 138 citations