David J. Miller

Artificial Intelligence · with Squicciarini · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it
• Safety, Robustness & Interpretability — 17 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Optimization & Training — 6 citations into it

Whom David J. Miller trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. L. Eon Bottou
4. Yoshua Bengio
5. Diederik P. Kingma
6. Geoffrey E. Hinton
7. Ilya Sutskever

Papers from David J. Miller's perspective

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

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

Their papers

• Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation (2018) — 353 citations
• Content-Driven Detection of Cyberbullying on the Instagram Social Network (2016) — 175 citations