Ahmed Touati

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 152 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #27,256 of 35,345 by trust here, #19,439 by citations
• Reinforcement Learning — 103 citations into it
• Generative Models — 51 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Management Science and Operations Research — 32 citations into it; #10,419 of 11,946 by trust here, #9,383 by citations
• Probabilistic & Bayesian Methods — 30 citations into it
• Optimization & Training — 27 citations into it
• Computational Theory and Mathematics — 14 citations into it

Whom Ahmed Touati trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Max Welling
7. Danilo Jimenez Rezende
8. Yann LeCun
9. Mohit Sewak
10. Doina Precup

Who trusts Ahmed Touati overall

1. Kaixiang Lin
2. Jalaj Bhandari
3. Scott Fujimoto
4. Khimya Khetarpal
5. Yue Wu
6. Jiayu Zhou
7. Daniel Russo
8. Doina Precup
9. David Meger
10. Ben M. Hambly

Papers from Ahmed Touati's perspective

Ordered by how strongly the researchers Ahmed Touati 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Randomized Value Functions via Multiplicative Normalizing Flows (2018) — 41 citations
• Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future (2019) — 55 citations
• Convergent Tree-Backup and Retrace with Function Approximation (2017) — 42 citations
• Learnable Explicit Density for Continuous Latent Space and Variational Inference (2017) — 28 citations
• Efficient Learning in Non-Stationary Linear Markov Decision Processes (2020) — 33 citations
• Learning One Representation to Optimize All Rewards (2021) — 128 citations
• Parametric Adversarial Divergences are Good Task Losses for Generative Modeling (2017) — 13 citations