Hany Hassan Awadalla
Artificial Intelligence · 2020–2025
A researcher in the Science corpus: 7 papers; trusts 89 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 175 citations into it; #28,535 of 35,345 by trust here, #8,893 by citations
- Computer Vision and Pattern Recognition — 69 citations into it; #17,523 of 21,575 by trust here, #11,083 by citations
- Natural Language Processing — 68 citations into it; #4,050 of 5,688 by trust here, #4,032 by citations
- Large Language Models — 60 citations into it; #583 of 799 by trust here, #537 by citations
- Evaluation & Benchmarks — 54 citations into it
- Efficiency & Compression — 32 citations into it; #693 of 1,264 by trust here, #693 by citations
- Model Architectures — 18 citations into it
- General Social Sciences — 10 citations into it
Whom Hany Hassan Awadalla trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Jacob Devlin
- Ashish Vaswani
- Alex Krizhevsky
- Ilya Sutskever
- Oriol Vinyals
- Noam Shazeer
- Kaiming He
Who trusts Hany Hassan Awadalla overall
Papers from Hany Hassan Awadalla's perspective
Ordered by how strongly the researchers Hany Hassan Awadalla trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Distilling the Knowledge in a Neural Network (arXiv.org)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Scalable and Efficient MoE Training for Multitask Multilingual Models (2021) — 101 citations
- DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders (2021) — 92 citations
- Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs (2025) — 509 citations
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
- Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness (2023) — 40 citations
- How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation (2023) — 603 citations
- FastFormers: Highly Efficient Transformer Models for Natural Language Understanding (2020) — 49 citations