Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Løland Anders"'
Publikováno v:
Dependence Modeling, Vol 9, Iss 1, Pp 62-81 (2021)
In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values. The original development of Shapley values for prediction explanation relied o
Externí odkaz:
https://doaj.org/article/225427ea4f4d4ef4bd46be33a51c8970
We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal components and then look at extensions to dynamic factor models for time series. Unlike principal components for t
Externí odkaz:
http://arxiv.org/abs/2212.08358
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by modeling the joint distribution
Externí odkaz:
http://arxiv.org/abs/2111.09790
There has been an intense recent activity in embedding of very high dimensional and nonlinear data structures, much of it in the data science and machine learning literature. We survey this activity in four parts. In the first part we cover nonlinear
Externí odkaz:
http://arxiv.org/abs/2106.01858
The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are dependent this may lead to incorrect explanations. Hence, there have rec
Externí odkaz:
http://arxiv.org/abs/2102.06416
Publikováno v:
BMC Genomics, Vol 6, Iss 1, p 147 (2005)
Abstract Background Global mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especi
Externí odkaz:
https://doaj.org/article/241f14620a3448f1bd8563d801b601f8
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Shapley values is a game
Externí odkaz:
http://arxiv.org/abs/1903.10464
Renewable energy sources provide a constantly increasing contribution to the total energy production worldwide. However, the power generation from these sources is highly variable due to their dependence on meteorological conditions. Accurate forecas
Externí odkaz:
http://arxiv.org/abs/1903.01186
We propose a fully probabilistic prediction model for spatially aggregated solar photovoltaic (PV) power production at an hourly time scale with lead times up to several days using weather forecasts from numerical weather prediction systems as covari
Externí odkaz:
http://arxiv.org/abs/1903.01188
Autor:
Myklebost Ola, Liu Fang, Rue Håvard, Langaas Mette, Holden Marit, Løland Anders, Nygaard Vigdis, Fodstad Øystein, Hovig Eivind, Smith-Sørensen Birgitte
Publikováno v:
BMC Genomics, Vol 4, Iss 1, p 11 (2003)
Abstract Background A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20–200 micro-grams total RNA or 0.5–2 micro-grams poly (A) RNA is typically required for monitoring gene
Externí odkaz:
https://doaj.org/article/fafd21a189ca4fdfbaed11bb2f5d57c6