Synthesizing benchmarks for predictive modeling
By Chris Cummins, Pavlos Petoumenos, Hugh Leather. 2017
121 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Hardware and Architecture, Software, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Lei Lyu
- Zhuoran Zheng
- Premkumar T. Devanbu
- Earl T. Barr
- Pavlos Petoumenos
- Xue Jiang
- Chris Cummins
- Hongyu Zhang
In Hardware and Architecture
In Software
- Charles Sutton
- Miltiadis Allamanis
- Earl T. Barr
- Premkumar T. Devanbu
- Pavlos Petoumenos
- Chris Cummins
- Hugh Leather
- Michael Pradel
In Evaluation & Benchmarks
- Miltiadis Allamanis
- Charles Sutton
- Earl T. Barr
- Premkumar T. Devanbu
- Songlin Hu
- Satish Chandra
- Rafael-Michael Karampatsis
- Michael Pradel
Citations in the corpus
- A Survey of Machine Learning for Big Code and Naturalness (2017, background): “[48] PD (c) Character RNN (LSTM) Benchmark Synthesis”
- End-to-end Deep Learning of Optimization Heuristics (2017, methodology): “In [7], a character based vocabulary is used.”
- HELoC: Hierarchical Contrastive Learning of Source Code Representation (2022, methodology): “Token-Based Model: Previous work [20, 52, 53]uses LSTM (long short-term memory) networks to model the input sequence of code tokens.”
- End-to-End Deep Learning of Optimization Heuristics (2017, methodology): “In [7], a character based vocabulary is used.”