Results 131 to 140 of about 8,972,433 (325)

Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks

IEEE Access
Prediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a
Jaehak Cho, Jae Myung Kim, Seungyub Han, Jungwoo Lee   +3 more
doaj   +1 more source

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

BMC Bioinformatics, 2020
Background Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes.
Ana Cernea, Juan Luis Fernández-Martínez, Enrique J. deAndrés-Galiana, Francisco Javier Fernández-Ovies, Oscar Alvarez-Machancoses, Zulima Fernández-Muñiz, Leorey N. Saligan, Stephen T. Sonis   +7 more
doaj   +1 more source

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  

Using machine learning techniques for sentiment analysis [PDF]

, 2017
The Natural language processing is the discipline that studies how to make the machines read and interpret the language that the people use, the natural language.
Romero Llombart, Òscar, Universitat Autònoma de Barcelona. Escola d'Enginyeria   +1 more
core  

Physics-informed machine learning

Nature Reviews Physics, 2021
G. Karniadakis, I. Kevrekidis, Lu Lu, P. Perdikaris, Sifan Wang, Liu Yang   +5 more
semanticscholar   +1 more source

A review of artificial intelligence in brachytherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen, Richard L. J. Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang   +4 more
wiley   +1 more source

Climate data selection for multi-decadal wind power forecasts

Environmental Research Letters
Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global climate models (GCMs) and regional climate models (RCMs) provide forecasts over multi-decadal periods.
Sofia Morelli, Nina Effenberger, Luca Schmidt, Nicole Ludwig   +3 more
doaj   +1 more source

CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data

IEEE Access, 2020
We propose a neural network architecture for detecting intrusions on the controller area network (CAN). The latter is the standard communication method between the electronic control units (ECUs) of automobiles. However, CAN lacks security mechanisms and
Markus Hanselmann, Thilo Strauss, Katharina Dormann, Holger Ulmer   +3 more
doaj   +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  

Mathematical Perspective of Machine Learning [PDF]

arXiv, 2020
We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.
arxiv