Ossama Abdel-Hamid

Artificial Intelligence · 2012–2014

A researcher in the Science corpus: 7 papers; trusts 120 researchers and is trusted by 163 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #1,625 of 35,345 by trust here, #2,359 by citations
• Signal Processing — 79 citations into it; #201 of 7,572 by trust here, #161 by citations
• Natural Language Processing — 35 citations into it
• Speech & Audio — 35 citations into it; #210 of 1,076 by trust here, #128 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Model Architectures — 9 citations into it; #461 of 2,278 by trust here, #409 by citations
• Optimization & Training — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Ossama Abdel-Hamid trusts overall

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

1. Abdel-rahman Mohamed
2. Li Deng
3. Dong Yu
4. Geoffrey E. Hinton
5. George E. Dahl
6. Yann LeCun
7. Yoshua Bengio
8. Frank Torsten Bernd Seide
9. Honglak Lee
10. Kai-Fu Lee

Who trusts Ossama Abdel-Hamid overall

1. Dong Yu
2. Li Deng
3. Karol J. Piczak
4. Jinyu Li
5. László Tóth
6. Yanmin Qian
7. Tara N. Sainath
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Navdeep Jaitly

Papers from Ossama Abdel-Hamid's perspective

Ordered by how strongly the researchers Ossama Abdel-Hamid 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition (2012) — 942 citations
• A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion (2013) — 184 citations
• Convolutional Neural Networks for Speech Recognition (2014) — 2,165 citations
• Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code (2013) — 235 citations
• Exploring convolutional neural network structures and optimization techniques for speech recognition (2013) — 417 citations
• Fast Adaptation of Deep Neural Network Based on Discriminant Codes for Speech Recognition (2014) — 155 citations
• Deep segmental neural networks for speech recognition (2013) — 46 citations