Saso Dzeroski

Molecular Biology · 2001–2019

A researcher in the Science corpus: 15 papers; trusts 66 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 77 citations into it; #3,126 of 17,773 by trust here, #4,339 by citations
• Artificial Intelligence — 35 citations into it; #6,126 of 35,345 by trust here, #10,065 by citations
• Information Systems — 17 citations into it; #838 of 8,771 by trust here, #2,260 by citations
• Evaluation & Benchmarks — 16 citations into it
• Genetics — 16 citations into it; #5,595 of 6,854 by trust here, #6,487 by citations
• Computational Biology & Bioinformatics — 15 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it; #16,638 of 21,575 by trust here, #14,151 by citations
• Algorithms & Complexity — 10 citations into it

Whom Saso Dzeroski trusts overall

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

1. J. Ross Quinlan
2. Thomas G. Dietterich
3. David William Aha
4. David Hilton Wolpert
5. Hendrik Blockeel
6. Rakesh Agrawal
7. Peter Clark
8. Thomas M. Cover
9. Dennis F. Kibler
10. Leslie Pack Kaelbling

Who trusts Saso Dzeroski overall

1. Lior Rokach
2. Maximilian Nickel
3. Yi Peng
4. Jonathan Cheung-Wai Chan
5. Clifton Phua
6. Shang He
7. Ahmed Ali Abbasi
8. Asaf Shabtai
9. Kristian Kersting
10. Kurt Driessens

Papers from Saso Dzeroski's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. The Nature of Statistical Learning Theory
8. ImageNet: A large-scale hierarchical image database
9. Multitask Learning
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Integrating Guidance into Relational Reinforcement Learning (2004) — 106 citations
• A large-scale evaluation of computational protein function prediction (2013) — 1,002 citations
• Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction (2013) — 51 citations
• Decision trees for hierarchical multi-label classification (2008) — 718 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• An extensive experimental comparison of methods for multi-label learning (2012) — 761 citations
• Multi-relational data mining: an introduction (2003) — 296 citations
• Yeasts and yeast-like fungi in tap water and groundwater, and their transmission to household appliances (2016) — 91 citations
• Speeding-Up Hoeffding-Based Regression Trees With Options (2011) — 50 citations
• Learning model trees from evolving data streams (2010) — 325 citations
• A qualitative multi-attribute model for economic and ecological assessment of genetically modified crops (2008) — 110 citations
• Is Combining Classifiers with Stacking Better than Selecting the Best One? (2004) — 936 citations
• Using regression trees to identify the habitat preference of the sea cucumber (Holothuria leucospilota) on Rarotonga, Cook Islands (2003) — 87 citations
• Relational Data Mining (2001) — 411 citations
• Habitat suitability modelling for red deer (Cervus elaphus L.) in South-central Slovenia with classification trees (2001) — 100 citations