Deepak Vijaykeerthy

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Causal Inference & Discovery — 6 citations into it
• Information Systems — 6 citations into it
• Signal Processing — 5 citations into it
• Software — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Deepak Vijaykeerthy trusts overall

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

1. Alex Krizhevsky
2. Judea Pearl
3. Geoffrey E. Hinton
4. Yann LeCun
5. Kaiming He
6. Karen Simonyan
7. Yoshua Bengio
8. Ian J. Goodfellow
9. Andrew Zisserman
10. L. Eon Bottou

Papers from Deepak Vijaykeerthy's perspective

Ordered by how strongly the researchers Deepak Vijaykeerthy 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Explaining Deep Learning Models using Causal Inference (2018) — 59 citations
• An Introduction to Adversarial Machine Learning (2017) — 12 citations
• Debugging Machine Learning Tasks (2016) — 47 citations