Anthony Stentz
Computer Vision and Pattern Recognition · 1985–2015
A researcher in the Science corpus: 16 papers; trusts 58 researchers and is trusted by 41 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 48 citations into it; #4,643 of 21,575 by trust here, #3,251 by citations
- Artificial Intelligence — 43 citations into it; #19,634 of 35,345 by trust here, #12,761 by citations
- Aerospace Engineering — 26 citations into it; #1,023 of 6,421 by trust here, #559 by citations
- Reinforcement Learning — 23 citations into it
- Natural Language Processing — 17 citations into it; #4,365 of 5,688 by trust here, #4,361 by citations
- Control and Systems Engineering — 16 citations into it; #1,272 of 8,257 by trust here, #995 by citations
- Robotics & Control — 12 citations into it
- Computer Networks and Communications — 11 citations into it; #2,119 of 9,886 by trust here, #2,687 by citations
Whom Anthony Stentz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maxim Likhachev
- Sven Koenig
- Nathan D. Ratliff
- Andrew Y. Ng
- D. Ferguson
- Brian P. Gerkey
- Geoffrey J. Gordon
- Reid G. Simmons
- Pieter Abbeel
- J. Bagnell
Who trusts Anthony Stentz overall
- J. Bagnell
- Nathan D. Ratliff
- David Silver
- Jens Kober
- Bo Liu
- Ola Ringdahl
- Harish Chaandar Ravichandar
- Maxim Likhachev
- Valts Blukis
- Mihail Pivtoraiko
Papers from Anthony Stentz's perspective
Ordered by how strongly the researchers Anthony Stentz 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
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Reinforcement Learning: An Introduction
Their papers
- Autonomous driving in urban environments: Boss and the Urban Challenge (2008) — 1,944 citations
- Learning models for following natural language directions in unknown environments (2015) — 93 citations
- Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain (2010) — 183 citations
- Imitation learning for natural language direction following through unknown environments (2013) — 61 citations
- Perceiving, learning, and exploiting object affordances for autonomous pile manipulation (2013) — 105 citations
- Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments (2006) — 299 citations
- Learning to Manipulate Unknown Objects in Clutter by Reinforcement (2015) — 100 citations
- Active learning from demonstration for robust autonomous navigation (2012) — 65 citations
- Multi-robot exploration controlled by a market economy (2002) — 647 citations
- Market-Based Multirobot Coordination: A Survey and Analysis (2006) — 1,016 citations
- A comprehensive taxonomy for multi-robot task allocation (2013) — 673 citations
- High Performance Outdoor Navigation from Overhead Data using Imitation Learning (2008) — 80 citations
- Replanning with RRTs (2006) — 351 citations
- Using interpolation to improve path planning: The Field D* algorithm (2006) — 435 citations
- Anytime Dynamic A*: An Anytime, Replanning Algorithm (2005) — 709 citations
- First Results in Robot Road-Following (1985)