Justus H. Piater
Computer Vision and Pattern Recognition · 2005–2016
A researcher in the Science corpus: 10 papers; trusts 54 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 53 citations into it; #8,967 of 21,575 by trust here, #7,421 by citations
- Artificial Intelligence — 38 citations into it; #7,763 of 35,345 by trust here, #9,641 by citations
- Reinforcement Learning — 38 citations into it; #2,120 of 2,242 by trust here, #1,864 by citations
- Control and Systems Engineering — 20 citations into it; #2,114 of 8,257 by trust here, #1,769 by citations
- Aerospace Engineering — 19 citations into it
- Biomedical Engineering — 13 citations into it; #3,233 of 10,734 by trust here, #6,312 by citations
- Robotics & Control — 11 citations into it; #212 of 609 by trust here, #333 by citations
- Statistical Learning Theory — 8 citations into it
Whom Justus H. Piater trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Dimitri P. Bertsekas
- Ben J. A. Kröse
- Richard S. Sutton
- Ryuichi Oka
- Andrew Barto
- David G. Lowe
- Niko Speybroeck
- Yuhai Wu
- Stephen W. Carden
- Erik B. Sudderth
Who trusts Justus H. Piater overall
- Rico Jonschkowski
- Luis Montesano
- Oliver Brock
- Thomas Lampe
- Jens Kober
- Manuel Lopes
- Tinne Tuytelaars
- Jason Ramapuram
- Massimo Minervini
- Antonin Raffin
Papers from Justus H. Piater's perspective
Ordered by how strongly the researchers Justus H. Piater 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Random subwindows for robust image classification (2005) — 270 citations
- Combining active learning and reactive control for robot grasping (2010) — 173 citations
- Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? (2013) — 389 citations
- Learning grasp affordance densities (2011) — 95 citations
- 25 Years of CNNs: Can We Compare to Human Abstraction Capabilities? (2016) — 58 citations
- Active learning using mean shift optimization for robot grasping (2009) — 34 citations
- Learning Visual Representations for Interactive Systems (2009) — 11 citations
- Online Learning of Gaussian Mixture Models - a Two-Level Approach (2008) — 75 citations
- Closed-Loop Learning of Visual Control Policies (2011) — 43 citations
- Learning visual representations for perception-action systems (2011) — 41 citations