An Experience Report on Machine Learning Reproducibility: Guidance for
Practitioners and TensorFlow Model Garden Contributors

An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors

2 July 2021

Akhil Chinnakotla

Anirudh Vegesana

Kruthi Krishnappa

Yung-Hsiang Lu

George K. Thiruvathukal

Papers citing "An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors"

8 / 8 papers shown

Title
YOLOv4: Optimal Speed and Accuracy of Object Detection Alexey Bochkovskiy Chien-Yao Wang H. Liao VLM ObjD 154 12,265 0 23 Apr 2020
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) Joelle Pineau Philippe Vincent-Lamarre Koustuv Sinha V. Larivière A. Beygelzimer Florence dÁlché-Buc E. Fox Hugo Larochelle 68 360 0 27 Mar 2020
Studying Software Engineering Patterns for Designing Machine Learning Systems Hironori Washizaki Hiromu Uchida Foutse Khomh Yann-Gaël Guéhéneuc 42 77 0 10 Oct 2019
Machine Learning Software Engineering in Practice: An Industrial Case Study M. S. Rahman Emilio Rivera Foutse Khomh Yann-Gaël Guéhéneuc Bernd Lehnert 25 45 0 17 Jun 2019
CenterNet: Keypoint Triplets for Object Detection Kaiwen Duan S. Bai Lingxi Xie H. Qi Qingming Huang Q. Tian NoLa 109 2,692 0 17 Apr 2019
YOLOv3: An Incremental Improvement Joseph Redmon Ali Farhadi ObjD 109 21,432 0 08 Apr 2018
YOLO9000: Better, Faster, Stronger Joseph Redmon Ali Farhadi VLM ObjD 179 15,610 0 25 Dec 2016
You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon S. Divvala Ross B. Girshick Ali Farhadi ObjD 682 36,923 0 08 Jun 2015