Pascal Sturmfels

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 91 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #25,570 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #17,521 of 21,575 by trust here, #18,266 by citations
• Safety, Robustness & Interpretability — 50 citations into it
• Molecular Biology — 36 citations into it
• Computational Biology & Bioinformatics — 18 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Optimization & Training — 11 citations into it
• Model Architectures — 10 citations into it

Whom Pascal Sturmfels trusts overall

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

1. Sean R. Eddy
2. Scott M. Lundberg
3. Geoffrey E. Hinton
4. Tunca Dogan
5. Alex Krizhevsky
6. Karen Simonyan
7. Yann LeCun
8. Christian Szegedy
9. Robert Tibshirani
10. Diederik P. Kingma

Who trusts Pascal Sturmfels overall

1. Jinjin Gu
2. Wojciech Samek
3. Chao Dong
4. Michael Sejr Schlichtkrull
5. Grgoire Montavon
6. Nicola De Cao

Papers from Pascal Sturmfels's perspective

Ordered by how strongly the researchers Pascal Sturmfels 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet Large Scale Visual Recognition Challenge
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Improving performance of deep learning models with axiomatic attribution priors and expected gradients (2020) — 355 citations
• Visualizing the Impact of Feature Attribution Baselines (2020) — 314 citations
• Learning Explainable Models Using Attribution Priors (2019) — 85 citations
• Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models (2020) — 27 citations