Sanjeeb Dash

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #9,387 of 35,345 by trust here, #14,718 by citations
• Statistical Learning Theory — 20 citations into it; #662 of 1,038 by trust here, #836 by citations
• Algorithms & Complexity — 19 citations into it; #296 of 516 by trust here, #278 by citations
• Computational Theory and Mathematics — 19 citations into it
• Information Systems — 15 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Safety, Robustness & Interpretability — 6 citations into it; #1,069 of 2,251 by trust here, #1,975 by citations
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Sanjeeb Dash trusts overall

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

1. Dmitry M. Malioutov
2. William W. Cohen
3. E.J. Candes
4. Tong Wang
5. Cynthia Rudin
6. Niko Speybroeck
7. Kush R. Varshney
8. Peter Ladislaw Hammer
9. Cynthia Barnhart
10. R. Rivest

Who trusts Sanjeeb Dash overall

1. Murat Firat
2. Yongxin Yang
3. Pantelis Linardatos
4. Jinqiang Yu
5. Oktay Günlük
6. Guillaume Crognier
7. Francesco Bodria
8. Irene Garcia Morillo
9. Riccardo Guidotti
10. Fosca Giannotti

Papers from Sanjeeb Dash's perspective

Ordered by how strongly the researchers Sanjeeb Dash 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. 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

• Learning interpretable classification rules using sequential rowsampling (2015) — 10 citations
• Screening for learning classification rules via Boolean compressed sensing (2014) — 10 citations
• Boolean Decision Rules via Column Generation (2018) — 200 citations
• Learning Interpretable Classification Rules with Boolean Compressed Sensing (2017) — 38 citations