Miha Škalič

Molecular Biology · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 4 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 30 citations into it; #14,100 of 17,773 by trust here, #9,136 by citations
• Computational Theory and Mathematics — 21 citations into it; #7,762 of 12,182 by trust here, #2,060 by citations
• Materials Chemistry — 17 citations into it; #5,616 of 11,513 by trust here, #6,536 by citations
• Computational Chemistry & Drug Discovery — 13 citations into it; #340 of 425 by trust here, #247 by citations

Whom Miha Škalič trusts overall

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

1. Marwin H. S. Segler
2. Sepp Hochreiter
3. Teague Sterling
4. Mariya Popova
5. Mark P. Waller
6. Matthew Ragoza
7. Marcus Olivecrona
8. J. Urgen Schmidhuber
9. Diederik P. Kingma
10. John J. Irwin

Who trusts Miha Škalič overall

1. Tomohide Masuda
2. Jiaqi Guan
3. Anastasiia V. Sadybekov
4. Shitong Luo
5. Vignesh Ram Somnath
6. Pedro H. M. Torres
7. Mostafa Karimi
8. Bian Li

Papers from Miha Škalič's perspective

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

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• The Inclusive Images Competition (2019) — 19 citations
• SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions (2018) — 593 citations
• KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks (2018) — 726 citations
• Shape-Based Generative Modeling for de Novo Drug Design (2019) — 208 citations