Pratik Jawanpuria

Artificial Intelligence · 2011–2015

A researcher in the Science corpus: 3 papers; trusts 44 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #15,122 of 35,345 by trust here, #27,551 by citations
• Representation & Self-Supervised Learning — 13 citations into it; #1,663 of 3,145 by trust here, #3,009 by citations
• Statistical Learning Theory — 12 citations into it
• Computational Mechanics — 11 citations into it; #3,926 of 10,001 by trust here, #7,173 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Optimization & Training — 9 citations into it
• Numerical Analysis — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Pratik Jawanpuria trusts overall

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

1. Francis R. Bach
2. Theodoros Evgeniou
3. Yu Zhang
4. Vladimir Naumovich Vapnik
5. Massimiliano Pontil
6. Aharon Ben-Tal
7. Yi Zhang
8. Yann LeCun
9. Laurent Jacob
10. Jerome H. Friedman

Who trusts Pratik Jawanpuria overall

1. Yu Zhang
2. Qiang Yang
3. Dmitry M. Malioutov
4. Tharindu Fernando
5. Kush R. Varshney
6. Maksim Lapin
7. Alon Zweig
8. Matthias Hein
9. Harshala Gammulle

Papers from Pratik Jawanpuria's perspective

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

Their papers

• A Convex Feature Learning Formulation for Latent Task Structure Discovery (2012) — 29 citations
• Efficient Output Kernel Learning for Multiple Tasks (2015) — 37 citations
• Efficient Rule Ensemble Learning using Hierarchical Kernels (2011) — 27 citations