Julian Bitterwolf

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 154 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #16,517 of 35,345 by trust here, #12,761 by citations
• Safety, Robustness & Interpretability — 64 citations into it; #1,084 of 2,251 by trust here, #1,119 by citations
• Computer Vision and Pattern Recognition — 61 citations into it; #10,518 of 21,575 by trust here, #9,187 by citations
• Evaluation & Benchmarks — 12 citations into it
• Optimization & Training — 11 citations into it
• Signal Processing — 10 citations into it
• Media Technology — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Julian Bitterwolf trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Yoshua Bengio
7. Dan Hendrycks
8. Yann LeCun
9. Ilya Sutskever
10. Ian J. Goodfellow

Who trusts Julian Bitterwolf overall

1. Alexander Meinke
2. Matthias Hein

Papers from Julian Bitterwolf's perspective

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

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

Their papers

• Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem (2018) — 630 citations
• A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions (2020) — 243 citations
• Increasing the robustness of DNNs against image corruptions by playing the Game of Noise (2020) — 66 citations