Abdeslam Boularias

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 8 papers; trusts 364 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #10,719 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 61 citations into it
• Reinforcement Learning — 53 citations into it; #519 of 2,242 by trust here, #1,163 by citations
• Control and Systems Engineering — 38 citations into it; #4,775 of 8,257 by trust here, #3,004 by citations
• Aerospace Engineering — 17 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it; #770 of 1,525 by trust here, #1,136 by citations
• Computational Theory and Mathematics — 11 citations into it
• Robotics & Control — 11 citations into it

Whom Abdeslam Boularias trusts overall

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

1. Andrew Y. Ng
2. Richard S. Sutton
3. Jan Peters
4. Pieter Abbeel
5. Stefan Schaal
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Brian D. Ziebart
9. Alex Krizhevsky
10. Kaiming He

Who trusts Abdeslam Boularias overall

1. Bilal Piot
2. Chelsea Finn
3. Sergey Levine
4. Kun Li
5. Pieter Abbeel
6. Jiachen Yang
7. Yannick Schroecker
8. Matthieu Geist
9. Abhishek Gupta
10. Ahmed Hussain Qureshi

Papers from Abdeslam Boularias's perspective

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

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

Their papers

• A self-supervised learning system for object detection using physics simulation and multi-view pose estimation (2017) — 118 citations
• Learning strategies in table tennis using inverse reinforcement learning (2014) — 81 citations
• Hilbert Space Embeddings of POMDPs (2012) — 51 citations
• Learning to Manipulate Unknown Objects in Clutter by Reinforcement (2015) — 100 citations
• Fast Model Identification via Physics Engines for Data-Efficient Policy Search (2017) — 53 citations
• Gradient Weights help Nonparametric Regressors (2012) — 8 citations
• Model Identification via Physics Engines for Improved Policy Search (2017) — 18 citations
• Relative Entropy Inverse Reinforcement Learning (2011) — 397 citations