Kuldeep S. Meel

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #15,332 of 35,345 by trust here, #29,412 by citations
• Computational Theory and Mathematics — 12 citations into it
• Statistical Learning Theory — 12 citations into it
• Safety, Robustness & Interpretability — 9 citations into it; #1,015 of 2,251 by trust here, #2,110 by citations
• Algorithms & Complexity — 8 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Kuldeep S. Meel trusts overall

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

1. William W. Cohen
2. Dmitry M. Malioutov
3. Peter Clark
4. Jessica Davies
5. Jerome H. Friedman
6. Mario Marchand
7. R. Rivest
8. Gert Cauwenberghs
9. Allison Chang
10. Kush R. Varshney

Who trusts Kuldeep S. Meel overall

1. Bishwamittra Ghosh
2. Henrik E. C. Cao
3. Jinqiang Yu

Papers from Kuldeep S. Meel's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. GENERATIVE ADVERSARIAL NETS
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019) — 37 citations
• MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules (2018) — 47 citations