Sehoon Ha
Reinforcement Learning · 2014–2023
A researcher in the Science corpus: 8 papers; trusts 213 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 116 citations into it; #1,589 of 2,242 by trust here, #667 by citations
- Artificial Intelligence — 113 citations into it; #10,000 of 35,345 by trust here, #5,788 by citations
- Control and Systems Engineering — 62 citations into it; #5,022 of 8,257 by trust here, #2,078 by citations
- Computer Vision and Pattern Recognition — 54 citations into it; #14,393 of 21,575 by trust here, #9,187 by citations
- Biomedical Engineering — 39 citations into it
- Robotics & Control — 22 citations into it
- Aerospace Engineering — 20 citations into it
- Computational Theory and Mathematics — 17 citations into it
Whom Sehoon Ha trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Pieter Abbeel
- Sergey Levine
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Yoshua Bengio
- Tuomas Haarnoja
- Ronald J. Williams
Who trusts Sehoon Ha overall
- Wenhao Yu
- Jie Tan
- Nan Jiang
- Greg Turk
- C. Karen Liu
- Quanyi Li
- Pieter Abbeel
- Xue Bin Peng
- Sergey Levine
- Zhaoming Xie
Papers from Sehoon Ha's perspective
Ordered by how strongly the researchers Sehoon Ha trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Soft Actor-Critic Algorithms and Applications (2018) — 3,200 citations
- DART: Dynamic Animation and Robotics Toolkit (2018) — 306 citations
- Learning to Walk via Deep Reinforcement Learning (2018) — 501 citations
- Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation (2021) — 41 citations
- Learning to Walk in the Real World with Minimal Human Effort (2020) — 224 citations
- Iterative Training of Dynamic Skills Inspired by Human Coaching Techniques (2014) — 400 citations
- Reducing hardware experiments for model learning and policy optimization (2015) — 25 citations
- VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation (2023) — 355 citations