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Articles by Pádraig Cunningham
[2511.07490] Have we reached the beginning of the end for review papers?
[Submitted on 10 Nov 2025] Title:Have we reached the beginning of the end for review papers? View a PDF of the paper titled Have we reached the beginning of the end for review papers?, by Barry Smyth and 1 other authors View PDF HTML (experimental) Abstract:Review papers have traditionally enjoyed a high status in academic publishing because of the important role they can play in summarising and synthesising a field of research.
The Price of Intelligence, Deepfakes, and More
by Mark Russinovich , Ahmed Salem , Santiago Zanella-Béguelin , and Yonatan Zunger Three risks inherent in LLMs. ... so our approach can never be based on eliminating them [hallucination, prompt injection, and jailbreaks]; rather, we should apply strategies of “defense in depth” to mitigate them, and when building and using these systems, do so on the assumption that they will sometimes fail in these directions.
An Analysis of the Impact of Gold Open Access Publications in Computer Science
Scientific publishing has changed significantly in recent years. Two important changes are the increase in volume and the move to open access (OA).17,20 The latter often requires authors to pay a publishing fee, introducing new commercial opportunities and attracting new publishers. Some of these new publishers have been branded as predatory21 because they are viewed to be exploiting the OA model without following the best practices to ensure scientific quality.
Feature Transformations: A Tutorial on PCA and LDA
Reducing the dimension of a dataset using methods such as PCA Introduction When dealing with high-dimension data, it is common to use methods such as Principal Component Analysis (PCA) to reduce the dimension of the data. This converts the data to a different (lower dimension) set of features. This contrasts with feature subset selection which selects a subset of the original features (see [1] for a turorial on feature selection).
Feature Subset Selection
A tutorial on feature selection and a recommended strategy Feature subset selection is important in supervised machine learning not just because it results in better models but also because of the insight it provides. This is particularly important now with the emphasis on interpretability in machine learning (ML). The challenge for practitioners is that there is a bewildering array of feature selection methods available.
Ensembles in Machine Learning
A Tutorial with Python Examples and Code Ensemble methods are well established as an algorithmic cornerstone in machine learning (ML). Just as in real life, in ML a committee of experts will often perform better than an individual provided appropriate care is taken in constituting the committee. Since the earliest days of ML research, a variety of ensemble strategies have been developed with random forests and gradient boosting emerging as leading-edge methods in classification today.
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