Marco Pavone
Artificial Intelligence · 2007–2023
A researcher in the Science corpus: 17 papers; trusts 774 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 166 citations into it; #8,512 of 35,345 by trust here, #7,531 by citations
- Computer Vision and Pattern Recognition — 157 citations into it; #6,480 of 21,575 by trust here, #8,952 by citations
- Reinforcement Learning — 69 citations into it; #1,721 of 2,242 by trust here, #1,822 by citations
- Management Science and Operations Research — 42 citations into it; #4,187 of 11,946 by trust here, #2,355 by citations
- Automotive Engineering — 37 citations into it; #205 of 3,081 by trust here, #996 by citations
- Control and Systems Engineering — 37 citations into it; #2,855 of 8,257 by trust here, #1,875 by citations
- Probabilistic & Bayesian Methods — 25 citations into it
- Generative Models — 24 citations into it
Whom Marco Pavone trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yoshua Bengio
- Radford M. Neal
- Kaiming He
- Sepp Hochreiter
- Alex Graves
- Yann LeCun
- J. Urgen Schmidhuber
Who trusts Marco Pavone overall
- Wei Ren
- Peng Lin
- Rick Zhang
- Cathy Wu
- Ye Yuan
- Javier Alonso-Mora
- Tianpei Gu
- Andrey Rudenko
- Kaiyu Qin
- Brian Ichter
Papers from Marco Pavone's perspective
Ordered by how strongly the researchers Marco Pavone 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning Sampling Distributions for Robot Motion Planning (2017) — 417 citations
- Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data (2020) — 1,349 citations
- Continuous Meta-Learning without Tasks (2019) — 84 citations
- The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs (2018) — 488 citations
- Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach (2015) — 412 citations
- Risk-Constrained Reinforcement Learning with Percentile Risk Criteria (2015) — 660 citations
- Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models (2017) — 72 citations
- FreeNeRF: Improving Few-Shot Neural Rendering with Free Frequency Regularization (2023) — 444 citations
- How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics (2017) — 234 citations
- Text2Motion: from natural language instructions to feasible plans (2023) — 408 citations
- Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks (2021) — 54 citations
- Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction (2017) — 187 citations
- Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore (2014) — 473 citations
- Control of robotic mobility-on-demand systems: A queueing-theoretical perspective (2014) — 425 citations
- Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions (2013) — 625 citations
- Robotic load balancing for mobility-on-demand systems (2012) — 305 citations
- Decentralized policies for geometric pattern formation (2007) — 183 citations