José A. Castellanos
Aerospace Engineering · 1999–2022
A researcher in the Science corpus: 5 papers; trusts 21 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Aerospace Engineering — 52 citations into it
- Computer Vision and Pattern Recognition — 52 citations into it
- Artificial Intelligence — 48 citations into it
- Reinforcement Learning — 35 citations into it
- Electrical and Electronic Engineering — 18 citations into it
- Control and Systems Engineering — 15 citations into it
- Computer Networks and Communications — 12 citations into it; #3,061 of 9,886 by trust here, #3,862 by citations
- Robotics & Control — 12 citations into it; #168 of 609 by trust here, #431 by citations
Whom José A. Castellanos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald R. Jones
- John J. Leonard
- Jan Peters
- Jan M. Maciejowski
- H. F. Durrant-Whyte
- Ronald J. Williams
- Daniel J. Lizotte
- Stefan Schaal
- Daniel E. Finkel
- Harold J. Kushner
Who trusts José A. Castellanos overall
- Sebastian Thrun
- Tim Bailey
- José Neira
- Juan D. Tardós
- Paul M. Newman
- Gemma Piella
- Frank Dellaert
- Wolfram Burgard
- Michael Kaess
- John J. Leonard
Papers from José A. Castellanos's perspective
Ordered by how strongly the researchers José A. Castellanos 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)
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- Asynchronous Methods for Deep Reinforcement Learning
- Human-level control through deep reinforcement learning
- Continuous control with deep reinforcement learning (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Information Theory: 1948-1998 - Guest Editorial
- Proximal Policy Optimization Algorithms (arXiv.org)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Their papers
- A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot (2009) — 256 citations
- A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022) — 339 citations
- Active Policy Learning for Robot Planning and Exploration under Uncertainty (2007) — 181 citations
- Mobile Robot Localization and Map Building: A Multisensor Fusion Approach (2000) — 311 citations
- The SPmap: a probabilistic framework for simultaneous localization and map building (1999)