SakethaNath Jagarlapudi

Artificial Intelligence · 2011–2014

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #14,596 of 35,345 by trust here, #31,361 by citations
• Representation & Self-Supervised Learning — 7 citations into it; #1,590 of 3,145 by trust here, #3,009 by citations
• Computational Mechanics — 6 citations into it; #3,757 of 10,001 by trust here, #8,566 by citations
• Statistical Learning Theory — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Numerical Analysis — 5 citations into it
• Optimization & Training — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom SakethaNath Jagarlapudi trusts overall

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

1. Francis R. Bach
2. Vladimir Naumovich Vapnik
3. Aharon Ben-Tal
4. Arthur Gretton
5. Bernhard Schölkopf
6. Jerome H. Friedman
7. Massimiliano Pontil
8. Theodoros Evgeniou
9. R. Rivest
10. Geoffrey E. Hinton

Who trusts SakethaNath Jagarlapudi overall

1. Yu Zhang
2. Qiang Yang
3. Dmitry M. Malioutov
4. Mingming Gong
5. Pratik Jawanpuria
6. Aviral Kumar
7. Kun Zhang
8. Xiyu Yu

Papers from SakethaNath Jagarlapudi's perspective

Ordered by how strongly the researchers SakethaNath Jagarlapudi 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• A Convex Feature Learning Formulation for Latent Task Structure Discovery (2012) — 29 citations
• Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection (2014) — 77 citations
• Efficient Rule Ensemble Learning using Hierarchical Kernels (2011) — 27 citations