them effectively is crucial to get good convergence in a reasonable amount of time. Exactly why stochastic gradients matter so much is still unknown, but some clues are emerging here and there. One of my favori
This is modelled as a bi-objective optimization problem which leads to the Pareto analysis. This in turn begets the question whether humans are Pareto-rational agents (i.e., take Pareto optimal decisions in the space of expected improvement and uncertainty). The analysis of computational results ...
Here are some of the prominent ones: Bayesian optimization: Bayesian optimization is a technique used in AutoML for hyperparameter tuning, which is the process of optimizing the parameters that are used to train a machine learning model. Bayesian optimization builds a probabilistic model of the ...
Accurately characterizing changes in irrigation water use (IWU) is crucial for formulating optimal water resource allocation policies, particularly in the context of climate change. However, existing IWU estimation methods suffer from uncertainties due to limited data availability and model constraints, rest...
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Hansard, or the official verbatim transcripts of parliamentary debates, contains rich information for analysing discourse and political activities on a wid
This approach is vital in adding a layer of semantic understanding and enhancing decision-making processes. On the other hand, non-symbolic AI encompassesmachine learning, deep learning, and neural networks, using vast amounts of training data to derive statistically informed conclusions and decisions...
Decisions regarding the shape of a building influence its architectural, aesthetic, and structural features as well as its sustainability. Shape affects brightness and heat loss, but also cost and usable area, to name just a few examples. As we will see in this review, there is active work ...
In the past 25 years, the importance of artificial intelligence has increased13. As scientists realize the importance of making firm decisions about how to treat certain diseases, the use of computers and machine learning as diagnostic tools has become deadly, which is the most serious disease ...
By leveraging such a large amount of structured and unstructured data, machine learning algorithms can support operations and enable informed decisions (Agrawal et al., 2020). Moreover, the growing availability of IoT that is the network of physical objects embedded with sensors, software, and ...