Masayoshi Tomizuka

Computer Vision and Pattern Recognition · 2001–2020

A researcher in the Science corpus: 10 papers; trusts 631 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 199 citations into it
• Artificial Intelligence — 125 citations into it; #28,674 of 35,345 by trust here, #7,346 by citations
• Reinforcement Learning — 47 citations into it
• Automotive Engineering — 42 citations into it; #2,100 of 3,081 by trust here, #2,035 by citations
• Aerospace Engineering — 35 citations into it
• Efficiency & Compression — 30 citations into it
• Model Architectures — 29 citations into it
• Control and Systems Engineering — 19 citations into it; #3,322 of 8,257 by trust here, #3,389 by citations

Whom Masayoshi Tomizuka trusts overall

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

1. Ross B. Girshick
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Sergey Levine
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Volodymyr Mnih

Who trusts Masayoshi Tomizuka overall

1. Bin Yao
2. Shuzhi Sam Ge
3. Pranav Singh Chib
4. Mou Chen
5. Andrew Palmer

Papers from Masayoshi Tomizuka's perspective

Ordered by how strongly the researchers Masayoshi Tomizuka 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Visual Transformers: Token-based Image Representation and Processing for Computer Vision (2020) — 755 citations
• Sparse R-CNN: End-to-End Object Detection with Learnable Proposals (2020) — 1,499 citations
• INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps (2019) — 610 citations
• SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation (2020) — 449 citations
• Model-free Deep Reinforcement Learning for Urban Autonomous Driving (2019) — 318 citations
• Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning (2020) — 339 citations
• Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection (2017) — 29 citations
• A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning (2017) — 90 citations
• Fast planning of well conditioned trajectories for model learning (2014) — 13 citations
• Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms (2001) — 349 citations