Franziska Biegler

Artificial Intelligence · 2012–2015

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it
• Statistical Learning Theory — 9 citations into it
• Statistics and Probability — 9 citations into it

Whom Franziska Biegler trusts overall

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

1. J. Behler

Who trusts Franziska Biegler overall

1. Tânia F Cova
2. Oliver T. Unke
3. Matthew J. Hirn
4. Chengqiang Lu
5. Peter Bjørn Jørgensen

Papers from Franziska Biegler's perspective

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

1. Gradient-based learning applied to document recognition
2. The Nature of Statistical Learning Theory
3. Natural Language Processing (Almost) from Scratch
4. The Elements of Statistical Learning
5. Stochastic Gradient Learning in Neural Networks
6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
7. ImageNet classification with deep convolutional neural networks
8. Neural Networks: Tricks of the Trade
9. Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. (2013) — 563 citations
• Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space (2015) — 732 citations
• Learning Invariant Representations of Molecules for Atomization Energy Prediction (2012) — 136 citations