Pavol Bielik

Information Systems · 2015–2016

A researcher in the Science corpus: 4 papers; trusts 31 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 45 citations into it; #482 of 8,771 by trust here, #1,220 by citations
• Artificial Intelligence — 39 citations into it; #4,775 of 35,345 by trust here, #9,275 by citations
• Natural Language Processing — 39 citations into it; #2,254 of 5,688 by trust here, #3,687 by citations
• Software — 31 citations into it; #124 of 1,227 by trust here, #222 by citations
• Code Generation & Program Synthesis — 25 citations into it; #8 of 220 by trust here, #18 by citations
• Signal Processing — 14 citations into it
• Program Analysis & Verification — 12 citations into it; #39 of 301 by trust here, #44 by citations
• Computational Theory and Mathematics — 8 citations into it

Whom Pavol Bielik trusts overall

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

1. Veselin Raychev
2. Premkumar T. Devanbu
3. Martin T. Vechev
4. Miltiadis Allamanis
5. Tung Thanh Nguyen
6. Anh Tuan Nguyen
7. Percy Liang
8. J. Ross Quinlan
9. Zhaopeng Tu
10. Fan Long

Who trusts Pavol Bielik overall

1. Miltiadis Allamanis
2. Michael Pradel
3. Uri Alon
4. Vijayaraghavan Murali
5. Daya Guo
6. Tomasz Korbak
7. Swarat Chaudhuri
8. Seohyun Kim
9. Maliheh Izadi
10. Koushik Sen

Papers from Pavol Bielik's perspective

Ordered by how strongly the researchers Pavol Bielik 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Probabilistic model for code with decision trees (2016) — 280 citations
• Learning programs from noisy data (2016) — 168 citations
• PHOG: Probabilistic Model for Code (2016) — 203 citations
• Programming with "Big Code": Lessons, Techniques and Applications (2015) — 22 citations