Nathalie Baracaldo

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 35 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #15,654 of 35,345 by trust here, #6,720 by citations
• Safety, Robustness & Interpretability — 72 citations into it; #1,721 of 2,251 by trust here, #1,047 by citations
• Privacy & Security — 61 citations into it; #540 of 709 by trust here, #586 by citations
• Federated & Distributed Learning — 35 citations into it
• Computer Networks and Communications — 23 citations into it
• Information Systems — 20 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Sociology and Political Science — 13 citations into it

Whom Nathalie Baracaldo trusts overall

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

1. Cynthia Dwork
2. Blaine Nelson
3. Ivan Bjerre Damgård
4. Battista Biggio
5. Reza Shokri
6. J. Ross Quinlan
7. Marco Barreno
8. Nicolas Papernot
9. Ilya Mironov
10. Oded Goldreich

Who trusts Nathalie Baracaldo overall

1. Clement Fung
2. Muhammad Shayan
3. Li Li
4. Haokun Fang
5. Xuefei Yin
6. Jiale Zhang
7. Bryant Chen
8. Latif Ullah Khan
9. Lingjuan Lyu
10. Heiko Ludwig

Papers from Nathalie Baracaldo's perspective

Ordered by how strongly the researchers Nathalie Baracaldo 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. Learning Multiple Layers of Features from Tiny Images
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
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 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