Michael Laskey

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 8 papers; trusts 116 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it
• Control and Systems Engineering — 73 citations into it; #1,688 of 8,257 by trust here, #188 by citations
• Artificial Intelligence — 67 citations into it; #22,681 of 35,345 by trust here, #7,413 by citations
• Reinforcement Learning — 50 citations into it; #1,993 of 2,242 by trust here, #1,204 by citations
• Aerospace Engineering — 41 citations into it
• Robotics & Control — 27 citations into it; #260 of 609 by trust here, #141 by citations
• Biomedical Engineering — 24 citations into it; #4,233 of 10,734 by trust here, #2,462 by citations
• Management Science and Operations Research — 10 citations into it

Whom Michael Laskey trusts overall

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

1. Stéphane Ross
2. Christopher K. I. Williams
3. John Schulman
4. Sergey Levine
5. Dean A. Pomerleau
6. Pieter Abbeel
7. Geoffrey E. Hinton
8. Alex Krizhevsky
9. Jonathan Ho
10. Vladimir Naumovich Vapnik

Who trusts Michael Laskey overall

1. Jeffrey Mahler
2. Ajay Mandlekar
3. Mayank Bansal
4. Ken Goldberg
5. Jiakai Zhang
6. Andreas Eitel
7. Yuping Luo
8. Yasasa Abeysirigoonawardena
9. Yuke Zhu
10. Sergey Levine

Papers from Michael Laskey's perspective

Ordered by how strongly the researchers Michael Laskey trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards (2016) — 391 citations
• DART: Noise Injection for Robust Imitation Learning (2017) — 328 citations
• SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces (2016) — 74 citations
• Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data (2020) — 145 citations
• Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations (2016) — 80 citations
• Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017) — 1,418 citations
• Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014) — 84 citations
• Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot (2017) — 28 citations