Nina Narodytska

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 8 papers; trusts 331 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #3,915 of 35,345 by trust here, #6,664 by citations
• Safety, Robustness & Interpretability — 90 citations into it; #452 of 2,251 by trust here, #939 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #5,429 of 21,575 by trust here, #8,296 by citations
• Computational Theory and Mathematics — 32 citations into it; #4,433 of 12,182 by trust here, #9,804 by citations
• Information Systems — 16 citations into it; #1,931 of 8,771 by trust here, #3,198 by citations
• Signal Processing — 16 citations into it
• Computer Networks and Communications — 14 citations into it; #3,251 of 9,886 by trust here, #8,543 by citations
• Hardware and Architecture — 9 citations into it; #1,360 of 1,551 by trust here, #1,153 by citations

Whom Nina Narodytska trusts overall

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

1. Karen Simonyan
2. Christian Szegedy
3. Nicolas Papernot
4. Ian J. Goodfellow
5. Andrew Zisserman
6. Geoffrey E. Hinton
7. Sergey Ioffe
8. Yann LeCun
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Nina Narodytska overall

1. Alexey Ignatiev
2. Emir Demirovi'c
3. J. Berg
4. Siddhant Bhambri
5. Peter James Stuckey
6. Francesco Croce
7. Jinqiang Yu
8. Andrew Ilyas
9. Weiwei Hu
10. Jiawei Su

Papers from Nina Narodytska's perspective

Ordered by how strongly the researchers Nina Narodytska 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Simple Black-Box Adversarial Perturbations for Deep Networks (2016) — 247 citations
• Simple Black-Box Adversarial Attacks on Deep Neural Networks (2017) — 321 citations
• Verifying Properties of Binarized Deep Neural Networks (2017) — 237 citations
• Maximum Satisfiability Using Core-Guided MaxSAT Resolution (2014) — 167 citations
• On Relating Explanations and Adversarial Examples (2019) — 135 citations
• A SAT-Based Approach to Learn Explainable Decision Sets (2018) — 63 citations
• Learning Optimal Decision Trees with SAT (2018) — 148 citations
• Abduction-Based Explanations for Machine Learning Models (2018) — 307 citations