Results 31 to 40 of about 414,531 (51)
Position Paper: Towards Transparent Machine Learning [PDF]
arXiv, 2019 Transparent machine learning is introduced as an alternative form of machine learning, where both the model and the learning system are represented in source code form. The goal of this project is to enable direct human understanding of machine learning models, giving us the ability to learn, verify, and refine them as programs.
arxiv
Machine Learning for Clinical Predictive Analytics [PDF]
arXiv, 2019 In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning
arxiv
Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML. [PDF]
J Big Data, 2022 Siriborvornratanakul T.
europepmc +1 more source
Differential Replication in Machine Learning [PDF]
arXiv, 2020 When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates.
arxiv
Introduction to intelligent computing unit 1 [PDF]
arXiv, 2017 This brief note highlights some basic concepts required toward understanding the evolution of machine learning and deep learning models. The note starts with an overview of artificial intelligence and its relationship to biological neuron that ultimately led to the evolution of todays intelligent models.
arxiv
Some Insights into Lifelong Reinforcement Learning Systems [PDF]
arXiv, 2020 A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. In this paper, I give some arguments to show that the traditional reinforcement learning paradigm fails to model this type of learning system.
arxiv
A scoping review on multimodal deep learning in biomedical images and texts. [PDF]
J Biomed Inform, 2023 Sun Z +6 more
europepmc +1 more source
Understanding Bias in Machine Learning [PDF]
1st Workshop on Visualization for AI Explainability in 2018 IEEE
Vis, 2019 Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can
arxiv
AI Research Funding Portfolios and Extreme Growth. [PDF]
Front Res Metr Anal, 2021 Rahkovsky I +4 more
europepmc +1 more source
Techniques for Automated Machine Learning [PDF]
arXiv, 2019 Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper,
arxiv