Mukund Deshpande

Information Systems · 2003–2005

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 83 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 37 citations into it; #254 of 8,771 by trust here, #1,246 by citations
• Artificial Intelligence — 20 citations into it
• Signal Processing — 19 citations into it; #418 of 7,572 by trust here, #1,147 by citations
• Computational Theory and Mathematics — 15 citations into it; #3,294 of 12,182 by trust here, #3,448 by citations
• Computer Networks and Communications — 13 citations into it
• Molecular Biology — 10 citations into it
• Algorithms & Complexity — 7 citations into it
• Statistical Learning Theory — 7 citations into it

Whom Mukund Deshpande trusts overall

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

1. Mohammed J. Zaki
2. George Karypis
3. Yuhai Wu
4. Michihiro Kuramochi
5. Jiawei Han
6. Rakesh Agrawal
7. Hans-Joachim Böhm
8. Jon M. Kleinberg
9. Paul Resnick
10. Steven L. Salzberg

Who trusts Mukund Deshpande overall

1. Yue Shi
2. Bamshad Mobasher
3. Charu C. Aggarwal
4. Fabio Aiolli
5. George Karypis
6. Hong Cheng
7. Amin Javari
8. Shilpa Arora
9. Jiawei Han
10. Xifeng Yan

Papers from Mukund Deshpande's perspective

Ordered by how strongly the researchers Mukund Deshpande 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Frequent substructure-based approaches for classifying chemical compounds (2003) — 307 citations
• Frequent sub-structure-based approaches for classifying chemical compounds (2005) — 294 citations
• Item-based top-N recommendation algorithms (2004) — 2,468 citations
• Selective Markov models for predicting Web page accesses (2004) — 574 citations