Results 11 to 20 of about 424,586 (329)

MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis [PDF]

, 2018
Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case ...
Anirudh, Rushil, Bremer, Timo, Sridhar, Rahul, Thiagarajan, Jayaraman J.   +3 more
core   +2 more sources

Prediction or interpretability?

Emerging Themes in Epidemiology, 2019
The journal published a review of the literature on recursive partition in epidemiological research comparing two decision tree methods: classification and regression trees (CARTs) and conditional inference trees (CITs).
Stefano Nembrini
doaj   +1 more source

Emulating quantum dynamics with neural networks via knowledge distillation

Frontiers in Materials, 2023
We introduce an efficient training framework for constructing machine learning-based emulators and demonstrate its capability by training an artificial neural network to predict the time evolution of quantum wave packets propagating through a potential ...
Yu Yao, Chao Cao, Stephan Haas, Mahak Agarwal, Divyam Khanna, Marcin Abram, Marcin Abram   +6 more
doaj   +1 more source

A novel interpretable model ensemble multivariate fast iterative filtering and temporal fusion transform for carbon price forecasting

Energy Science & Engineering, 2023
The accurate forecasts of carbon prices can help policymakers and enterprises further understand the laws of carbon price fluctuations and formulate related policies and investment strategies.
Yue Wang, Zhong Wang, Xinyu Kang, Yuyan Luo   +3 more
doaj   +1 more source

Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework

Frontiers in Surgery, 2023
ObjectivePostoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications.
Yifeng Chen, Xiaoyu Cai, Zicheng Cao, Jie Lin, Wenyu Huang, Yuan Zhuang, Lehan Xiao, Xiaozhen Guan, Ying Wang, Xingqiu Xia, Feng Jiao, Xiangjun Du, Guozhi Jiang, Guozhi Jiang, Deqing Wang   +14 more
doaj   +1 more source

Interpreting (un)interpretability [PDF]

Theoretical Linguistics, 2019
published
Walkden, George, Breitbarth, Anne
openaire   +3 more sources

Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability

Frontiers in Oncology, 2022
BackgroundMicrosatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counterpart is challenging in clinical practice ...
Jin Zhu, Wangwei Wu, Yuting Zhang, Shiyun Lin, Yukang Jiang, Ruixian Liu, Heping Zhang, Xueqin Wang   +7 more
doaj   +1 more source

Explainable Machine Learning for Scientific Insights and Discoveries

IEEE Access, 2020
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences ...
Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke   +3 more
doaj   +1 more source

Decision Support System Improving the Interpretability of Generated Tree-Based Models

Acta Electrotechnica et Informatica, 2022
A decision tree represents one of the most used data analysis methods for classification tasks. The generated decision models can be visualized as a graph, but this visualization is quite complicated for a domain expert to understand in large or ...
Klimonová Diana, Anderková Viera, Babič František, Majnaric Ljiljana Trtica   +3 more
doaj   +1 more source

Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods

IEEE Access, 2022
Deep Learning (DL) based classification algorithms have been shown to achieve top results in clinical diagnosis, namely with lung cancer datasets. However, the complexity and opaqueness of the models together with the still scant training datasets call ...
Mafalda Malafaia, Francisco Silva, Ines Neves, Tania Pereira, Helder P. Oliveira   +4 more
doaj   +1 more source