Vikas Chandrakant Raykar

Artificial Intelligence · 2006–2014

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #22,308 of 35,345 by trust here, #6,330 by citations
• Natural Language Processing — 18 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Optimization & Training — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Vikas Chandrakant Raykar trusts overall

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

1. Christopher K. I. Williams
2. George Eastman House
3. Volker Tresp
4. Radford M. Neal
5. R. Caruana
6. Eibe Frank
7. Arthur P. Dempster
8. Lehel Csató
9. Geoffrey E. Hinton
10. Shai Fine

Who trusts Vikas Chandrakant Raykar overall

1. Jakramate Bootkrajang
2. Zhi-Hua Zhou
3. Hongwei Li
4. Alistair E. W. Johnson
5. Dan Zhang
6. Ata Kabán
7. Dengyong Zhou
8. Filipe Rodrigues
9. Justin Cheng
10. Jeroen B. P. Vuurens

Papers from Vikas Chandrakant Raykar's perspective

Ordered by how strongly the researchers Vikas Chandrakant Raykar 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
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

• An Autoencoder Approach to Learning Bilingual Word Representations (2014) — 348 citations
• Bayesian multiple instance learning: automatic feature selection and inductive transfer (2008) — 160 citations
• Learning From Crowds (2010) — 1,543 citations
• Supervised learning from multiple experts: whom to trust when everyone lies a bit (2009) — 391 citations
• Fast large scale Gaussian process regression using approximate matrix-vector products (2006)