Sadid A. Hasan

Artificial Intelligence · with Liu · 2014–2019

A researcher in the Science corpus: 7 papers; trusts 43 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #12,471 of 35,345 by trust here, #17,110 by citations
• Natural Language Processing — 59 citations into it; #2,366 of 5,688 by trust here, #3,897 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Model Architectures — 20 citations into it
• Reinforcement Learning — 18 citations into it
• Medical & Health AI — 14 citations into it
• Molecular Biology — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom Sadid A. Hasan trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Krizhevsky
7. Kaiming He
8. Andrew Barto
9. Yann LeCun
10. Alex Graves

Who trusts Sadid A. Hasan overall

1. Yang Gao
2. Christian M. Meyer
3. Chao Yu
4. Iryna Gurevych
5. Brielen Madureira
6. Asma Ben Abacha
7. Stefan Henß
8. Yue Dong
9. Florian Böhm
10. Jiming Liu

Papers from Sadid A. Hasan's perspective

Ordered by how strongly the researchers Sadid A. Hasan 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. Long Short-Term Memory
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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (2018) — 105 citations
• Condensed Memory Networks for Clinical Diagnostic Inferencing (2016) — 108 citations
• VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019 (2019) — 322 citations
• Overview of ImageCLEF 2018 Medical Domain Visual Question Answering Task (2018) — 117 citations
• Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning (2017) — 29 citations
• Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study (2017) — 43 citations
• Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning (2014) — 41 citations