Veselin Raychev
Artificial Intelligence · 2014–2019
A researcher in the Science corpus: 9 papers; trusts 66 researchers and is trusted by 177 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 66 citations into it; #1,750 of 35,345 by trust here, #3,687 by citations
- Information Systems — 63 citations into it; #51 of 8,771 by trust here, #158 by citations
- Natural Language Processing — 54 citations into it; #576 of 5,688 by trust here, #1,130 by citations
- Software — 37 citations into it; #4 of 1,227 by trust here, #9 by citations
- Code Generation & Program Synthesis — 26 citations into it; #3 of 220 by trust here, #1 by citations
- Program Analysis & Verification — 17 citations into it; #7 of 301 by trust here, #7 by citations
- Signal Processing — 15 citations into it; #486 of 7,572 by trust here, #1,105 by citations
- Computational Theory and Mathematics — 10 citations into it
Whom Veselin Raychev trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Miltiadis Allamanis
- Premkumar T. Devanbu
- Martin T. Vechev
- Daphne Koller
- R. Rosenfeld
- Armando Solar-Lezama
- Franz Joseph Och
- John D. Lafferty
- J. Ross Quinlan
- Sumit Gulwani
Who trusts Veselin Raychev overall
- Miltiadis Allamanis
- Michael Pradel
- Vijayaraghavan Murali
- Swarat Chaudhuri
- Vincent J. Hellendoorn
- Uri Alon
- Charles Sutton
- Premkumar T. Devanbu
- Marc Brockschmidt
- Pavol Bielik
Papers from Veselin Raychev's perspective
Ordered by how strongly the researchers Veselin Raychev trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Code completion with statistical language models (2014) — 722 citations
- Probabilistic model for code with decision trees (2016) — 280 citations
- Phrase-Based Statistical Translation of Programming Languages (2014) — 147 citations
- Learning programs from noisy data (2016) — 168 citations
- Predicting program properties from 'big code' (2019) — 18 citations
- Statistical Deobfuscation of Android Applications (2016) — 133 citations
- PHOG: Probabilistic Model for Code (2016) — 203 citations
- Programming with "Big Code": Lessons, Techniques and Applications (2015) — 22 citations
- Predicting Program Properties from "Big Code" (2015) — 405 citations