Seyedmahdad Mirsamadi

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it; #24,744 of 35,345 by trust here, #34,213 by citations
• Signal Processing — 16 citations into it; #3,332 of 7,572 by trust here, #4,373 by citations
• Speech & Audio — 10 citations into it; #760 of 1,076 by trust here, #1,060 by citations
• Model Architectures — 6 citations into it
• Experimental and Cognitive Psychology — 5 citations into it; #4,793 of 9,731 by trust here, #6,168 by citations

Whom Seyedmahdad Mirsamadi trusts overall

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

1. Alex Graves
2. Sepp Hochreiter
3. Yoshua Bengio
4. Dzmitry Bahdanau
5. J. Urgen Schmidhuber
6. Geoffrey E. Hinton
7. Kyunghyun Cho
8. Dong Yu
9. Junyoung Chung
10. Shashidhar G. Koolagudi

Who trusts Seyedmahdad Mirsamadi overall

1. Shahram Ghorbani
2. Seunghyun Yoon
3. Monorama Swain
4. Ahmet Emin Bulut
5. Yan Wang
6. Leonardo Pepino
7. Seokhyun Byun
8. Aurobinda Routray
9. John H. L. Hansen

Papers from Seyedmahdad Mirsamadi's perspective

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

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On Multi-Domain Training and Adaptation of End-to-End RNN Acoustic Models for Distant Speech Recognition (2017) — 22 citations
• Automatic speech emotion recognition using recurrent neural networks with local attention (2017) — 670 citations