Heiko Ludwig

Artificial Intelligence · 2000–2020

A researcher in the Science corpus: 14 papers; trusts 35 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 177 citations into it; #15,655 of 35,345 by trust here, #6,720 by citations
• Safety, Robustness & Interpretability — 72 citations into it; #1,720 of 2,251 by trust here, #1,047 by citations
• Privacy & Security — 62 citations into it; #538 of 709 by trust here, #586 by citations
• Federated & Distributed Learning — 36 citations into it
• Computer Networks and Communications — 28 citations into it; #6,604 of 9,886 by trust here, #6,498 by citations
• Information Systems — 22 citations into it; #5,571 of 8,771 by trust here, #5,501 by citations
• Computational Theory and Mathematics — 14 citations into it
• Sociology and Political Science — 12 citations into it

Whom Heiko Ludwig trusts overall

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

1. Cynthia Dwork
2. J. Ross Quinlan
3. Ivan Bjerre Damgård
4. Ilya Mironov
5. Blaine Nelson
6. H. Brendan McMahan
7. Mikhail Yurochkin
8. Martín Abadi
9. Reza Shokri
10. Nathalie Baracaldo

Who trusts Heiko Ludwig overall

1. Pankesh Patel
2. Gerardo Canfora
3. Fan Lai
4. Ajith Ranabahu
5. Amit P. Sheth
6. Li Li
7. Massimiliano Di Penta
8. Yinwei Dai
9. Haokun Fang

Papers from Heiko Ludwig's perspective

Ordered by how strongly the researchers Heiko Ludwig 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Intriguing properties of neural networks
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Hybrid Approach to Privacy-Preserving Federated Learning (2018) — 1,112 citations
• Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering (2018) — 980 citations
• Adversarial Robustness Toolbox v1.0.0 (2018) — 573 citations
• IBM Federated Learning: an Enterprise Framework White Paper V0.1 (2020) — 198 citations
• TiFL: A Tier-based Federated Learning System (2020) — 370 citations
• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019) — 321 citations
• Towards Federated Graph Learning for Collaborative Financial Crimes Detection (2019) — 82 citations
• Towards Taming the Resource and Data Heterogeneity in Federated Learning (2019) — 94 citations
• Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach (2017) — 125 citations
• Web services agreement specification (ws-agreement) (2004) — 1,049 citations
• Web services on demand: WSLA-driven automated management (2004) — 321 citations
• Web Service Level Agreement (WSLA) Language Specification (2003) — 649 citations
• The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services (2003) — 1,094 citations
• CrossFlow: Cross-Organizational Workflow Management in Dynamic Virtual Enterprises (2000) — 404 citations