Viktoriya Krakovna

Artificial Intelligence · 2016

A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 8 citations into it; #14,934 of 35,345 by trust here, #25,809 by citations
• Safety, Robustness & Interpretability — 6 citations into it; #987 of 2,251 by trust here, #1,994 by citations

Whom Viktoriya Krakovna trusts overall

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

1. Hervé Bourlard
2. Ivan Titov
3. Vinayak A. Rao
4. Andrej Karpathy
5. Andrew L. Maas
6. Yee Whye Teh
7. Geoffrey E. Hinton
8. Nelson Morgan
9. Quoc V. Le
10. Yoshua Bengio

Who trusts Viktoriya Krakovna overall

1. Menaka Narayanan
2. Avery Hiebert
3. Emily Chen
4. Isaac Lage
5. Alejandro Barredo Arrieta
6. Jeffrey He
7. Cole Peterson
8. Been Kim
9. Natalia Díaz Rodríguez
10. Alona Fyshe

Papers from Viktoriya Krakovna's perspective

Ordered by how strongly the researchers Viktoriya Krakovna 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models (2016) — 71 citations