Seyedamin Pouriyeh

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 12 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #33,755 of 35,345 by trust here, #14,181 by citations
• Natural Language Processing — 32 citations into it
• Information Systems — 25 citations into it
• Information Retrieval & Search — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Seyedamin Pouriyeh trusts overall

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

1. David M. Blei
2. Dragomir R. Radev
3. Robert E. Schapire
4. Leo Breiman
5. Andrew McCallum
6. Chin-Yew Lin
7. Thomas Hofmann
8. Thorsten Joachims
9. C. Burges
10. T. Griffiths

Who trusts Seyedamin Pouriyeh overall

1. Ammar Ismael Kadhim
2. Tongshuang Sherry Wu

Papers from Seyedamin Pouriyeh's perspective

Ordered by how strongly the researchers Seyedamin Pouriyeh 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Text Summarization Techniques: A Brief Survey (2017) — 558 citations
• A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017) — 567 citations
• A survey on security and privacy of federated learning (2021) — 1,391 citations
• Federated-Learning-Based Anomaly Detection for IoT Security Attacks (2021) — 590 citations