Rekha Khanna

Computational Mechanics · 2021

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 13 citations into it
• Artificial Intelligence — 11 citations into it
• Optimization & Training — 8 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Statistics and Probability — 7 citations into it
• Biomedical Engineering — 5 citations into it

Whom Rekha Khanna trusts overall

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

1. Trevor Campbell
2. Max Welling
3. Tamara Broderick
4. Martin Jaggi
5. Joel A. Tropp
6. Thomas Blumensath
7. Dougal Maclaurin
8. Wei Dai
9. Richard Baraniuk
10. Matthew D. Hoffman

Papers from Rekha Khanna's perspective

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

Their papers

• Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective (2021) — 23 citations