Daniel Cullina

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 85 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 166 citations into it; #30,635 of 35,345 by trust here, #23,077 by citations
• Safety, Robustness & Interpretability — 135 citations into it
• Computer Vision and Pattern Recognition — 55 citations into it
• Signal Processing — 51 citations into it; #6,606 of 7,572 by trust here, #4,560 by citations
• Computer Networks and Communications — 34 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Information Systems — 11 citations into it
• Molecular Biology — 11 citations into it

Whom Daniel Cullina trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Jonathon Shlens
5. Ian J. Goodfellow
6. Christian Szegedy
7. Christopher K. I. Williams
8. Yoshua Bengio
9. Ilya Sutskever
10. Kaiming He

Who trusts Daniel Cullina overall

1. Nicholas Carlini
2. David Stutz
3. Naveed Akhtar
4. Ke He
5. Zihao Liu
6. Weilin Xu
7. Negin Entezari
8. Nilaksh Das

Papers from Daniel Cullina's perspective

Ordered by how strongly the researchers Daniel Cullina 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Enhancing robustness of machine learning systems via data transformations (2017) — 246 citations
• Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples (2019) — 27 citations
• Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers (2017) — 164 citations