Pavel Brazdil

Artificial Intelligence · 1995–2015

A researcher in the Science corpus: 9 papers; trusts 28 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #4,007 of 35,345 by trust here, #8,283 by citations
• Statistical Learning Theory — 15 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Information Systems — 8 citations into it; #650 of 8,771 by trust here, #1,815 by citations
• Algorithms & Complexity — 7 citations into it
• Evaluation & Benchmarks — 7 citations into it; #923 of 3,147 by trust here, #2,009 by citations
• Computer Vision and Pattern Recognition — 6 citations into it; #9,841 of 21,575 by trust here, #15,162 by citations
• Statistics and Probability — 6 citations into it; #3,028 of 12,932 by trust here, #3,801 by citations

Whom Pavel Brazdil trusts overall

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

1. William W. Cohen
2. J. Ross Quinlan
3. Johannes Fürnkranz
4. Donald Michie
5. João Gama
6. Sepp Hochreiter
7. J. Urgen Schmidhuber
8. B. D. Ripley
9. Hilan Bensusan
10. Yann LeCun

Who trusts Pavel Brazdil overall

1. Matthias Reif
2. Frank Hutter
3. Jan N. van Rijn
4. Christiane Lemke
5. Katharina Eggensperger
6. Carlos Soares
7. K. Smith‐Miles
8. Adam Santoro
9. Ricardo Vilalta
10. Marc-André Zöller

Papers from Pavel Brazdil's perspective

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

Their papers

• Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results (2003) — 473 citations
• Introduction to the Special Issue on Meta-Learning (2004) — 184 citations
• A Meta-Learning Method to Select the Kernel Width in Support Vector Regression (2004) — 201 citations
• Fast Algorithm Selection Using Learning Curves (2015) — 87 citations
• Meta-Learning (2009) — 212 citations
• Cost-Sensitive Decision Trees Applied to Medical Data (2007) — 93 citations
• Using Meta-Learning to Support Data Mining (2004) — 393 citations
• Cascade Generalization (2000) — 300 citations
• Characterization of Classification Algorithms (1995)