H. S. Anderson

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 6 papers; trusts 58 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #24,298 of 35,345 by trust here, #8,999 by citations
• Safety, Robustness & Interpretability — 65 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it
• Signal Processing — 51 citations into it; #5,022 of 7,572 by trust here, #3,913 by citations
• Computer Networks and Communications — 24 citations into it; #6,394 of 9,886 by trust here, #4,950 by citations
• Information Systems — 24 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Natural Language Processing — 22 citations into it

Whom H. S. Anderson trusts overall

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

1. William W. Cohen
2. Ian J. Goodfellow
3. Richard S. Sutton
4. J. Kolter
5. Joshua Saxe
6. Nicolas Papernot
7. Geoffrey E. Hinton
8. Matthew G. Schultz
9. Christian Szegedy
10. Yoshua Bengio

Who trusts H. S. Anderson overall

1. Mengying Sun
2. Giorgio Severi
3. Xiaoyong Yuan
4. Zhibo Zhang
5. Daniel S. Berman
6. Fengyi Tang
7. Yuanshun Yao
8. J. Meyer
9. Anant Kharkar
10. Pan He

Papers from H. S. Anderson's perspective

Ordered by how strongly the researchers H. S. Anderson 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (2018) — 923 citations
• Poisoning Web-Scale Training Datasets is Practical (2023) — 362 citations
• EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models (2018) — 657 citations
• Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning (2018) — 245 citations
• Evading Machine Learning Malware Detection (2017)
• DeepDGA: Adversarially-Tuned Domain Generation and Detection (2016) — 223 citations