Brendan Avent

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 420 citations into it; #34,703 of 35,345 by trust here, #3,338 by citations
• Privacy & Security — 230 citations into it; #685 of 709 by trust here, #526 by citations
• Optimization & Training — 96 citations into it
• Safety, Robustness & Interpretability — 81 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• Computational Theory and Mathematics — 47 citations into it
• Sociology and Political Science — 42 citations into it; #22,284 of 22,756 by trust here, #11,144 by citations
• Computer Science Applications — 29 citations into it

Whom Brendan Avent trusts overall

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

1. Cynthia Dwork
2. Ilya Mironov
3. Frank McSherry
4. H. Brendan McMahan
5. L. Eon Bottou
6. Diederik P. Kingma
7. Kunal Talwar
8. Geoffrey E. Hinton
9. Borja Balle
10. Ron Kohavi

Who trusts Brendan Avent overall

1. Yatharth Dubey
2. Ying Zhao
3. Yingxue Zhou
4. Nicolas Papernot
5. Aleksandra Korolova
6. Jinyuan Jia
7. Jinjun Chen
8. Tianhao Wang
9. Zhiwei Steven Wu
10. Abhradeep Thakurta

Papers from Brendan Avent's perspective

Ordered by how strongly the researchers Brendan Avent 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. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
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

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017) — 102 citations
• Automatic Discovery of Privacy–Utility Pareto Fronts (2019) — 32 citations