Richard P. Lippmann

Computer Networks and Communications · 1987–2010

A researcher in the Science corpus: 10 papers; trusts 26 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 29 citations into it; #893 of 9,886 by trust here, #448 by citations
• Signal Processing — 22 citations into it; #970 of 7,572 by trust here, #538 by citations
• Artificial Intelligence — 20 citations into it; #16,129 of 35,345 by trust here, #8,157 by citations
• Information Systems — 16 citations into it; #937 of 8,771 by trust here, #1,175 by citations
• Safety, Robustness & Interpretability — 9 citations into it; #782 of 2,251 by trust here, #1,731 by citations
• Statistical Learning Theory — 8 citations into it
• Natural Language Processing — 7 citations into it
• Privacy & Security — 6 citations into it; #288 of 709 by trust here, #512 by citations

Whom Richard P. Lippmann trusts overall

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

1. Ke Wang
2. Amir Globerson
3. John J. Hopfield
4. Bradley Efron
5. Prahlad Fogla
6. James Newsome
7. Salvatore J. Stolfo
8. William W. Cohen
9. Sam T. Roweis
10. Ofer Dekel

Who trusts Richard P. Lippmann overall

1. A. Waibel
2. Lambros Ekonomou
3. Konrad Rieck
4. M. V. D. Baan
5. Harry Wechsler
6. James Jian Qiao Yu
7. Dong Yu
8. Tian-Shyug Lee
9. Matthew V. Mahoney
10. Li Deng

Papers from Richard P. Lippmann's perspective

Ordered by how strongly the researchers Richard P. Lippmann 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Automated generation and analysis of attack graphs (2002) — 1,458 citations
• Testing static analysis tools using exploitable buffer overflows from open source code (2004) — 308 citations
• Machine learning in adversarial environments (2010) — 140 citations
• Learning to Detect and Classify Malicious Executables in the Wild (2006) — 664 citations
• Practical Attack Graph Generation for Network Defense (2006) — 413 citations
• Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation (2000) — 1,144 citations
• The 1999 DARPA off-line intrusion detection evaluation (2000) — 1,014 citations
• Speech recognition by machines and humans (1997)
• An introduction to computing with neural nets (1987)
• Multi-style training for robust isolated-word speech recognition (1987)