Ahmed Khalifa

Artificial Intelligence · with Togelius · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #23,801 of 35,345 by trust here, #12,083 by citations
• Reinforcement Learning — 38 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #18,398 of 21,575 by trust here, #11,950 by citations
• Natural Language Processing — 12 citations into it
• Model Architectures — 8 citations into it; #1,894 of 2,278 by trust here, #2,037 by citations
• Optimization & Training — 7 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Ahmed Khalifa trusts overall

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

1. Ilya Sutskever
2. Alex Graves
3. Yoshua Bengio
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Paul J. Werbos
7. Kyunghyun Cho
8. Dzmitry Bahdanau
9. Sepp Hochreiter
10. Alex Krizhevsky

Who trusts Ahmed Khalifa overall

1. Pradyumna Tambwekar
2. Louis Castricato
3. Brent Harrison
4. Lara J. Martin
5. Karl Cobbe
6. Murtaza Dhuliawala
7. Mark O. Riedl

Papers from Ahmed Khalifa's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation (2018) — 205 citations
• Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning (2019) — 151 citations
• DeepTingle (2017) — 32 citations