Ajit Paul Singh

Artificial Intelligence · 2008–2011

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 110 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #2,442 of 35,345 by trust here, #6,664 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Molecular Biology — 10 citations into it
• Computational Biology & Bioinformatics — 8 citations into it
• Optimization & Training — 8 citations into it
• Computational Mechanics — 7 citations into it; #1,479 of 10,001 by trust here, #4,450 by citations

Whom Ajit Paul Singh trusts overall

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

1. Nati Srebro
2. Jason D. M. Rennie
3. Eric R. Ziegel
4. Shenghuo Zhu
5. David A. Cohn
6. Shipeng Yu
7. Michael Collins
8. Thomas Hofmann
9. Daniel D. Lee
10. Arindam Banerjee

Who trusts Ajit Paul Singh overall

1. Qiang Yang
2. Qian Xu
3. Hao Wang
4. Weike Pan
5. Yu Zheng
6. Marinka Zitnik
7. Yu Zhang
8. Guangneng Hu
9. Gabriella Csurka
10. Ting-Li Chen

Papers from Ajit Paul Singh's perspective

Ordered by how strongly the researchers Ajit Paul Singh trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Relational learning via collective matrix factorization (2008) — 1,416 citations
• Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure (2011) — 43 citations
• A Unified View of Matrix Factorization Models (2008) — 196 citations