Michael Pradel
Information Systems · 2016–2024
A researcher in the Science corpus: 12 papers; trusts 168 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 207 citations into it; #563 of 8,771 by trust here, #1,362 by citations
- Software — 150 citations into it; #240 of 1,227 by trust here, #394 by citations
- Artificial Intelligence — 134 citations into it; #14,099 of 35,345 by trust here, #17,412 by citations
- Natural Language Processing — 72 citations into it; #1,347 of 5,688 by trust here, #2,767 by citations
- Code Generation & Program Synthesis — 69 citations into it; #40 of 220 by trust here, #98 by citations
- Program Analysis & Verification — 69 citations into it; #25 of 301 by trust here, #39 by citations
- Signal Processing — 66 citations into it; #1,298 of 7,572 by trust here, #2,221 by citations
- Computer Networks and Communications — 35 citations into it
Whom Michael Pradel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Veselin Raychev
- Miltiadis Allamanis
- Martin T. Vechev
- Tomas Mikolov
- Patrice Godefroid
- Ilya Sutskever
- Pavol Bielik
- Premkumar T. Devanbu
- Diederik P. Kingma
- Earl T. Barr
Who trusts Michael Pradel overall
- Rabee Sohail Malik
- Milod Kazerounian
- Miltiadis Allamanis
- Kevin Jesse
- Yi Li
- Rafael-Michael Karampatsis
- Earl T. Barr
- Amir M. Mir
- Charles Sutton
- Max Schäfer
Papers from Michael Pradel's perspective
Ordered by how strongly the researchers Michael Pradel trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- TypeWriter: neural type prediction with search-based validation (2019) — 135 citations
- Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts (2017) — 65 citations
- RepairAgent: An Autonomous, LLM-Based Agent for Program Repair (2024) — 387 citations
- Fuzz4ALL: Universal Fuzzing with Large Language Models (2023) — 326 citations
- Scaffle: bug localization on millions of files (2020) — 40 citations
- Small World with High Risks: A Study of Security Threats in the npm Ecosystem (2019) — 309 citations
- NL2Type: Inferring JavaScript Function Types from Natural Language Information (2019) — 138 citations
- Anything to Hide? Studying Minified and Obfuscated Code in the Web (2019) — 57 citations
- Automated program repair (2019) — 489 citations
- DeepBugs: a learning approach to name-based bug detection (2018) — 396 citations
- Deep Learning to Find Bugs (2017) — 34 citations
- Learning to Fuzz: Application-Independent Fuzz Testing with Probabilistic, Generative Models of Input Data (2016) — 65 citations