It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. The popularity of DL … The Machine Learning Reproducibility Crisis. Model outputs are critically dependent on the data and processing approach used to 05/12/2020 ∙ by Brandon Houghton, et al. I was recently chatting to a friend whose startup’s machine learning models were so disorganized it was causing serious problems as his team tried to build on each other’s work and share it with clients. A new class of machine learning algorithm, deep learning algorithms, outperforms the results of clas … Medical image segmentation is an important tool for current clinical applications. Deep networks are gradually penetrating almost every domain in our lives due to their amazing success. ∙ Carnegie Mellon University ∙ 3 ∙ share . Finally, we propose 3 main recommendations to address these potential issues: (1) Deep learning does, however, have the potential to reduce the reproducibility of scientific results. March 19, 2018 By Pete Warden in Uncategorized 40 Comments. Deep learning, a set of approaches using artificial neural networks, has generated rapid recent advance-ments in machine learning. Deep learning requires expensive GPU powered machines and in companies where data scientists lack the skill set to manage these, it could potentially mean hundreds to … Gosper Glider Gun. Deep learning, a set of approaches using artificial neural networks, has generated rapid recent advancements in machine learning. Two identical models, trained on the exact same training dataset may exhibit large differences in predictions on individual examples even when average accuracy is similar, … Guaranteeing Reproducibility in Deep Learning Competitions. Model outputs are critically dependent on … However, with substantive performance accuracy improvements comes the price of \\emph{irreproducibility}. Deep learning does, however, have the potential to reduce the reproducibility of scientific results. understand the challenges and issues of reproducibility related to deep learning for medical image segmentation. To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance of their learning procedures rather than pre-trained agents. Deep learning (DL) has become a key branch of Machine Learning (ML) (lecun2015deep; schmidhuber2015deep; hanprogrammers), and now is a core component in systems for many aspects of modern society, such as autonomous cars (tian2018deeptest), medical image diagnosis (litjens2017survey), financial market prediction (fischer2018deep), etc.