Jason Martin

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Safety, Robustness & Interpretability — 23 citations into it

Whom Jason Martin trusts overall

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

1. Christian Szegedy
2. Nicholas Carlini
3. Shaoqing Ren
4. Kaiming He
5. Joseph Redmon
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Anish Athalye
9. Jiajun Lu

Papers from Jason Martin's perspective

Ordered by how strongly the researchers Jason Martin trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
7. Rethinking the Inception Architecture for Computer Vision
8. Intriguing properties of neural networks
9. Long Short-Term Memory
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector (2018) — 480 citations
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data (2020) — 1,493 citations
• Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation (2018) — 583 citations