Mahmoud Khademi

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 2 papers; trusts 35 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #4,873 of 35,345 by trust here, #8,439 by citations
• Natural Language Processing — 34 citations into it
• Information Systems — 13 citations into it; #501 of 8,771 by trust here, #1,086 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Graph Learning — 8 citations into it; #128 of 693 by trust here, #223 by citations
• Signal Processing — 8 citations into it
• Code Generation & Program Synthesis — 7 citations into it
• Software — 7 citations into it

Whom Mahmoud Khademi trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Kaiming He
7. Miltiadis Allamanis
8. Yann LeCun
9. Tomas Mikolov
10. Veselin Raychev

Who trusts Mahmoud Khademi overall

1. Uri Alon
2. Miltiadis Allamanis
3. Shaked Brody
4. Aditya Kanade
5. Eran Yahav
6. Kevin Jesse
7. Marc Brockschmidt
8. Daniel Tarlow
9. Petros Maniatis
10. Michihiro Yasunaga

Papers from Mahmoud Khademi's perspective

Ordered by how strongly the researchers Mahmoud Khademi 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Learning to Represent Programs with Graphs (2017) — 919 citations
• i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2023) — 4 citations