Kevin Maik Jablonka

Materials Chemistry · 2020–2024

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 213 citations into it
• Computational Theory and Mathematics — 112 citations into it
• Artificial Intelligence — 99 citations into it
• Inorganic Chemistry — 80 citations into it
• Computational Chemistry & Drug Discovery — 45 citations into it
• Molecular Biology — 40 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Atomic and Molecular Physics, and Optics — 27 citations into it

Whom Kevin Maik Jablonka trusts overall

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

1. Cynthia Rudin
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. J. Behler
7. Yann LeCun
8. Peter Ertl
9. Ilya Sutskever
10. Brenden M. Lake

Who trusts Kevin Maik Jablonka overall

1. Seyed Mohamad Moosavi
2. Daniil A. Boiko
3. Andrés M Bran
4. Robert MacKnight

Papers from Kevin Maik Jablonka's perspective

Ordered by how strongly the researchers Kevin Maik Jablonka 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning (2020) — 469 citations
• Leveraging large language models for predictive chemistry (2024) — 403 citations
• Understanding the diversity of the metal-organic framework ecosystem (2020) — 487 citations
• The Role of Machine Learning in the Understanding and Design of Materials (2020) — 345 citations