Παρακολούθηση
Rahul Yedida
Rahul Yedida
PhD Candidate, North Carolina State University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα ncsu.edu - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
Έτος
On the Value of Oversampling for Deep Learning in Software Defect Prediction
R Yedida, T Menzies
IEEE Transactions on Software Engineering, 2021
642021
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
R Yedida, S Saha, T Prashanth
Applied Intelligence, 1-19, 2020
59*2020
Employee Attrition Prediction
R Yedida, R Reddy, R Vahi, R Jana, A GV, D Kulkarni
arXiv preprint arXiv:1806.10480, 2018
422018
Learning to recognize actionable static code warnings (is intrinsically easy)
X Yang, J Chen, R Yedida, Z Yu, T Menzies
Empirical Software Engineering 26 (3), 1-24, 2021
40*2021
Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets
S Saha, N Nagaraj, A Mathur, R Yedida, S HR
The European Physical Journal Special Topics 229 (16), 2629-2738, 2020
392020
Simpler hyperparameter optimization for software analytics: Why, how, when?
A Agrawal, X Yang, R Agrawal, R Yedida, X Shen, T Menzies
IEEE Transactions on Software Engineering 48 (8), 2939-2954, 2021
262021
How to find actionable static analysis warnings: A case study with FindBugs
R Yedida, HJ Kang, H Tu, X Yang, D Lo, T Menzies
IEEE Transactions on Software Engineering 49 (4), 2856-2872, 2023
182023
How to improve deep learning for software analytics: (a case study with code smell detection)
R Yedida, T Menzies
Proceedings of the 19th International Conference on Mining Software …, 2022
162022
An expert system for redesigning software for cloud applications
R Yedida, R Krishna, A Kalia, T Menzies, J Xiao, M Vukovic
Expert Systems with Applications, 2023
14*2023
Old but Gold: Reconsidering the value of feedforward learners for software analytics
R Yedida, X Yang, T Menzies
arXiv preprint arXiv:2101.06319, 2021
11*2021
Lessons learned from hyper-parameter tuning for microservice candidate identification
R Yedida, R Krishna, A Kalia, T Menzies, J Xiao, M Vukovic
2021 36th IEEE/ACM International Conference on Automated Software …, 2021
82021
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets
S Sridhar, S Saha, A Shaikh, R Yedida, S Saha
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
82020
Beginning with machine learning: a comprehensive primer
R Yedida, S Saha
The European Physical Journal Special Topics 230 (10), 2363-2444, 2021
62021
(Re) Use of Research Results (Is Rampant)
MT Baldassarre, N Ernst, B Hermann, T Menzies, R Yedida
Communications of the ACM 66 (2), 75-81, 2023
5*2023
Finding a good learning rate
R Yedida
Blog post, 2019
22019
Documenting evidence of a reuse of ‘a systematic study of the class imbalance problem in convolutional neural networks’
R Yedida, T Menzies
Proceedings of the 29th ACM Joint Meeting on European Software Engineering …, 2021
12021
Workshop on Replications and Negative Results
R Yedida, T Menzies
2024
Guidelines for the Application of Neural Technologies in Software Analytics (or: How to Do More with Less in SE).
R Yedida
2024
Strong convexity-guided hyper-parameter optimization for flatter losses
R Yedida, S Saha
arXiv preprint arXiv:2402.05025, 2024
2024
Is Hyper-Parameter Optimization Different for Software Analytics?
R Yedida, T Menzies
arXiv preprint arXiv:2401.09622, 2024
2024
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