Benjamin C. M. Fung

Artificial Intelligence · 2003–2020

A researcher in the Science corpus: 12 papers; trusts 99 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 157 citations into it; #5,352 of 35,345 by trust here, #6,330 by citations
• Privacy & Security — 47 citations into it; #620 of 709 by trust here, #336 by citations
• Sociology and Political Science — 46 citations into it; #1,731 of 22,756 by trust here, #2,063 by citations
• Information Systems — 23 citations into it; #4,723 of 8,771 by trust here, #6,770 by citations
• Electrical and Electronic Engineering — 22 citations into it
• Management Science and Operations Research — 21 citations into it; #2,244 of 11,946 by trust here, #4,746 by citations
• Computational Theory and Mathematics — 19 citations into it; #7,407 of 12,182 by trust here, #8,756 by citations
• Signal Processing — 17 citations into it; #4,520 of 7,572 by trust here, #5,829 by citations

Whom Benjamin C. M. Fung trusts overall

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

1. Cynthia Dwork
2. Latanya Sweeney
3. Vijay S. Iyengar
4. Pierangela Samarati
5. Ke Wang
6. Charu C. Aggarwal
7. Frank McSherry
8. Philip S. Yu
9. Rakesh Agrawal
10. Xiaokui Xiao

Who trusts Benjamin C. M. Fung overall

1. Charu C. Aggarwal
2. Daniel Kifer
3. Philip S. Yu
4. Ashwin Machanavajjhala
5. Kristen LeFevre
6. Rob Hall
7. Ke Wang
8. Rui Chen
9. Xiaokui Xiao
10. Tiancheng Li

Papers from Benjamin C. M. Fung's perspective

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

Their papers

• Privacy-preserving data publishing: A survey of recent developments (2010) — 1,918 citations
• Top-down specialization for information and privacy preservation (2005) — 686 citations
• Anonymizing Classification Data for Privacy Preservation (2007) — 430 citations
• Differentially private data release for data mining (2011) — 378 citations
• Hierarchical Document Clustering using Frequent Itemsets (2003) — 523 citations
• Correlated network data publication via differential privacy (2013) — 235 citations
• Security and privacy challenges in smart cities (2018) — 307 citations
• A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020) — 459 citations
• Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization (2019) — 469 citations
• Secure Distributed Framework for Achieving ε-Differential Privacy (2012) — 45 citations
• A systematic procedure to study the influence of occupant behavior on building energy consumption (2011) — 586 citations
• Privacy-preserving data publishing (2007) — 164 citations