Philipp Seidl
Artificial Intelligence · 2020–2022
A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 57 citations into it
- Computational Theory and Mathematics — 34 citations into it
- Materials Chemistry — 34 citations into it
- Computer Vision and Pattern Recognition — 30 citations into it
- Computational Chemistry & Drug Discovery — 24 citations into it
- Molecular Biology — 17 citations into it
- Optimization & Training — 9 citations into it
- Physical and Theoretical Chemistry — 5 citations into it
Whom Philipp Seidl trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Daniel M. Lowe
- Marwin H. S. Segler
- Geoffrey E. Hinton
- Ilya Loshchilov
- Kaiming He
- Connor W. Coley
- Yoshua Bengio
- Alex Krizhevsky
- Diederik P. Kingma
- Jimmy Ba
Who trusts Philipp Seidl overall
Papers from Philipp Seidl's perspective
Ordered by how strongly the researchers Philipp Seidl trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- Hopfield Networks is All You Need (2020) — 701 citations
- Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks (2022) — 90 citations
- Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction (2021) — 10 citations