Blaz Zupan
Molecular Biology · 2004–2020
A researcher in the Science corpus: 13 papers; trusts 63 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 100 citations into it; #4,487 of 17,773 by trust here, #5,550 by citations
- Artificial Intelligence — 62 citations into it; #7,325 of 35,345 by trust here, #11,921 by citations
- Representation & Self-Supervised Learning — 28 citations into it; #1,552 of 3,145 by trust here, #2,330 by citations
- Computer Vision and Pattern Recognition — 21 citations into it; #7,794 of 21,575 by trust here, #10,010 by citations
- Genetics — 17 citations into it
- Biophysics — 15 citations into it
- Information Systems — 13 citations into it
- Computational Theory and Mathematics — 12 citations into it
Whom Blaz Zupan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ajit Paul Singh
- Igor Kononenko
- Sunil Rao
- L. V. D. van der Maaten
- J. Ross Quinlan
- Leland McInnes
- Klaus Nordhausen
- Alan R. Aronson
- Geoffrey E. Hinton
- Gert R. G. Lanckriet
Who trusts Blaz Zupan overall
- Marinka Zitnik
- Jaka Demsar
- Ping Wang
- D. Ahiaga-Dagbui
- Riccardo Bellazzi
- Erik Strumbelj
- Yifeng Li
- Hadi Banaee
- Jure Leskovec
- Riccardo Miotto
Papers from Blaz Zupan's perspective
Ordered by how strongly the researchers Blaz Zupan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Orange: From Experimental Machine Learning to Interactive Data Mining (2004) — 522 citations
- Data Fusion by Matrix Factorization (2013) — 209 citations
- iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution (2010) — 1,169 citations
- openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (2019) — 231 citations
- Characterising the RNA targets and position-dependent splicing regulation by TDP-43; implications for neurodegenerative diseases (2011) — 1,040 citations
- Embedding to reference t-SNE space addresses batch effects in single-cell classification (2019) — 36 citations
- scOrange—a tool for hands-on training of concepts from single-cell data analytics (2019) — 13 citations
- What can education bring to entrepreneurship? Formal versus non-formal education (2020) — 112 citations
- From hype to reality: data science enabling personalized medicine (2018) — 359 citations
- COLLECTIVE PAIRWISE CLASSIFICATION FOR MULTI-WAY ANALYSIS OF DISEASE AND DRUG DATA (2016) — 23 citations
- Orange: data mining toolbox in python (2013) — 2,143 citations
- Predictive data mining in clinical medicine: Current issues and guidelines (2008) — 870 citations
- Nomograms for visualizing support vector machines (2005) — 92 citations