Saikiran Rapaka

Computational Mechanics · 2009–2018

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 11 citations into it

Whom Saikiran Rapaka trusts overall

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

1. Radford M. Neal
2. Yoshua Bengio
3. Dazhi Yu
4. Markus Uhlmann

Papers from Saikiran Rapaka's perspective

Ordered by how strongly the researchers Saikiran Rapaka trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning Deep Architectures for AI
2. ImageNet classification with deep convolutional neural networks
3. Pattern Recognition and Machine Learning
4. Gradient-based learning applied to document recognition
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve: Result From the MACHINE Consortium (2018) — 341 citations
• A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. (2016) — 380 citations
• Flow patterns in the sedimentation of an elliptical particle (2009) — 159 citations