Purushottam Kar
Artificial Intelligence · 2012–2018
A researcher in the Science corpus: 7 papers; trusts 151 researchers and is trusted by 66 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 63 citations into it; #4,681 of 35,345 by trust here, #9,870 by citations
- Computational Mechanics — 62 citations into it
- Computer Vision and Pattern Recognition — 39 citations into it; #4,980 of 21,575 by trust here, #11,286 by citations
- Optimization & Training — 21 citations into it; #1,313 of 2,601 by trust here, #1,766 by citations
- Statistics and Probability — 21 citations into it; #4,933 of 12,932 by trust here, #5,294 by citations
- Statistical Learning Theory — 20 citations into it; #323 of 1,038 by trust here, #569 by citations
- Signal Processing — 17 citations into it
- Information Systems — 13 citations into it; #7,218 of 8,771 by trust here, #7,107 by citations
Whom Purushottam Kar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christopher K. I. Williams
- Joel A. Tropp
- Chih-Jen Lin
- B. Recht
- Yurii E. Nesterov
- Prateek Jain
- E.J. Candes
- Ingo Steinwart
- Geoffrey E. Hinton
- Yoshua Bengio
Who trusts Purushottam Kar overall
- Shu Kong
- Per-Gunnar Martinsson
- Ninh D. Pham
- Haim Avron
- Felix X. Yu
- Jeffrey Pennington
- Mingsheng Long
- Krikamol Muandet
- David Lopez-Paz
- Ankit Singh Rawat
Papers from Purushottam Kar's perspective
Ordered by how strongly the researchers Purushottam Kar 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
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Random Feature Maps for Dot Product Kernels (2012) — 272 citations
- Large-scale Multi-label Learning with Missing Labels (2013) — 515 citations
- Non-convex Optimization for Machine Learning (2017) — 550 citations
- Robust Regression via Hard Thresholding (2015) — 169 citations
- Compilation Error Repair: For the Student Programs, From the Student Programs (2018) — 100 citations
- Consistent Robust Regression (2017) — 105 citations
- Sparse Local Embeddings for Extreme Multi-label Classification (2015) — 464 citations