Felix A. Faber

Computational Theory and Mathematics · 2015–2017

A researcher in the Science corpus: 6 papers; trusts 5 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 46 citations into it
• Artificial Intelligence — 14 citations into it
• Statistical Learning Theory — 13 citations into it
• Optimization & Training — 8 citations into it
• Statistics and Probability — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Felix A. Faber trusts overall

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

1. Christopher K. I. Williams
2. Ian T. Jolliffe
3. James Franklin
4. Klaus Nordhausen
5. R. Ramakrishnan
6. Matthias Rupp 
7. A. P. Bart'ok
8. Kristof T. Schütt
9. Vladimir G Vovk
10. K. Müller

Who trusts Felix A. Faber overall

1. Sam Schoenholz
2. Tânia F Cova
3. Johannes Klicpera
4. Logan T. Ward
5. Clemens Isert
6. Ilyes Batatia
7. Kristof T. Schütt
8. Manzil Zaheer
9. A. Pais
10. Peter Bjørn Jørgensen

Papers from Felix A. Faber's perspective

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

Their papers

• Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. (2017) — 593 citations
• Crystal structure representations for machine learning models of formation energies (2015) — 421 citations
• Alchemical and structural distribution based representation for universal quantum machine learning. (2017) — 323 citations
• Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. (2015) — 305 citations
• Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy (2017) — 19 citations
• Machine learning prediction errors better than DFT accuracy (2017) — 36 citations