M. Pawan Kumar
Artificial Intelligence · 2005–2019
A researcher in the Science corpus: 9 papers; trusts 596 researchers and is trusted by 1,134 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 96 citations into it; #12,576 of 35,345 by trust here, #17,110 by citations
- Computer Vision and Pattern Recognition — 70 citations into it; #1,297 of 21,575 by trust here, #7,421 by citations
- Optimization & Training — 42 citations into it; #376 of 2,601 by trust here, #537 by citations
- Safety, Robustness & Interpretability — 26 citations into it
- Computational Mechanics — 19 citations into it
- Model Architectures — 11 citations into it; #1,753 of 2,278 by trust here, #2,128 by citations
- Computational Theory and Mathematics — 10 citations into it
- Program Analysis & Verification — 10 citations into it
Whom M. Pawan Kumar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vladimir Kolmogorov
- Pedro F. Felzenszwalb
- Yuri Boykov
- Yoshua Bengio
- Navneet Dalal
- Yann LeCun
- Kaiming He
- Geoffrey E. Hinton
- Ross B. Girshick
- John K. Kruschke
Who trusts M. Pawan Kumar overall
- Lu Jiang
- Andrew Zisserman
- Deyu Meng
- Li Fei-Fei
- Yi Yang
- Dingwen Zhang
- Abhinav Kumar Gupta
- Jitendra Malik
- Yong Jae Lee
- Xinlei Chen
Papers from M. Pawan Kumar's perspective
Ordered by how strongly the researchers M. Pawan Kumar trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- OBJ CUT (2005) — 413 citations
- Dissimilarity Coefficient Based Weakly Supervised Object Detection (2018) — 89 citations
- MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts (2009) — 39 citations
- Piecewise Linear Neural Network verification: A comparative study (2017) — 73 citations
- Training Neural Networks for and by Interpolation (2019) — 72 citations
- A Statistical Approach to Assessing Neural Network Robustness (2018) — 99 citations
- Statistical Verification of Neural Networks (2018) — 7 citations
- Modeling Latent Variable Uncertainty for Loss-based Learning (2012) — 23 citations
- Self-Paced Learning for Latent Variable Models (2010) — 1,658 citations