Lukas Pirch

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it
• Safety, Robustness & Interpretability — 15 citations into it
• Privacy & Security — 12 citations into it
• Optimization & Training — 5 citations into it

Whom Lukas Pirch trusts overall

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

1. Cynthia Dwork
2. Alex Graves
3. Yann LeCun
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Alex Krizhevsky
7. Pang Wei Koh
8. Frank Hampel
9. Yoshua Bengio
10. Kamalika Chaudhuri

Papers from Lukas Pirch's perspective

Ordered by how strongly the researchers Lukas Pirch 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. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
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

• Machine Unlearning of Features and Labels (2021) — 322 citations