Barry Devereux
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View article: A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges Open
This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and May 2025. A total of 128 articles met our inclusion criteria.…
View article: Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition
Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition Open
Traditional models of reading lack a realistic simulation of the early visual processing stages, taking input in the form of letter banks and predefined line segments, making them unsuitable for modeling early brain responses. We used vari…
View article: Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition
Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction
SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction Open
Table extraction from document images is a challenging AI problem, and labelled data for many content domains is difficult to come by. Existing table extraction datasets often focus on scientific tables due to the vast amount of academic a…
View article: Convolutional networks can model the functional modulation of MEG responses during reading
Convolutional networks can model the functional modulation of MEG responses during reading Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: Reviewer #2 (Public Review): Convolutional networks can model the functional modulation of MEG responses during reading
Reviewer #2 (Public Review): Convolutional networks can model the functional modulation of MEG responses during reading Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: Author response: Convolutional networks can model the functional modulation of MEG responses during reading
Author response: Convolutional networks can model the functional modulation of MEG responses during reading Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: Reviewer #1 (Public Review): Convolutional networks can model the functional modulation of MEG responses during reading
Reviewer #1 (Public Review): Convolutional networks can model the functional modulation of MEG responses during reading Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition
Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition Open
Traditional models of reading lack a realistic simulation of the early visual processing stages, taking input in the form of letter banks and predefined line segments, making them unsuitable for modeling early brain responses. We used vari…
View article: Reviewer #3 (Public Review): Convolutional networks can model the functional modulation of MEG responses during reading
Reviewer #3 (Public Review): Convolutional networks can model the functional modulation of MEG responses during reading Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM
QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM Open
The BioNLP ACL'24 Shared Task on Streamlining Discharge Documentation aims to reduce the administrative burden on clinicians by automating the creation of critical sections of patient discharge letters. This paper presents our approach usi…
View article: How Is a “Kitchen Chair” like a “Farm Horse”? Exploring the Representation of Noun-Noun Compound Semantics in Transformer-based Language Models
How Is a “Kitchen Chair” like a “Farm Horse”? Exploring the Representation of Noun-Noun Compound Semantics in Transformer-based Language Models Open
Despite the success of Transformer-based language models in a wide variety of natural language processing tasks, our understanding of how these models process a given input in order to represent task-relevant information remains incomplete…
View article: On the similarities of representations in artificial and brain neural networks for speech recognition
On the similarities of representations in artificial and brain neural networks for speech recognition Open
Introduction In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of …
View article: On the similarities of representations in artificial and brain neural networks for speech recognition
On the similarities of representations in artificial and brain neural networks for speech recognition Open
How the human brain supports speech comprehension is an important question in neuroscience. Studying the neurocomputational mechanisms underlying human language is not only critical to understand and develop treatments for many human condi…
View article: A zero-shot deep metric learning approach to Brain–Computer Interfaces for image retrieval
A zero-shot deep metric learning approach to Brain–Computer Interfaces for image retrieval Open
In this paper we propose a deep learning based approach for image retrieval using EEG. Our approach makes use of a multi-modal deep neural network based on metric learning, where the EEG signal from a user observing an image is mapped toge…
View article: Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition
Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition Open
Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulati…
View article: A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers
A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers Open
Brain-Computer Interfaces (BCIs) enable converting the brain electrical activity of an interface user to the user commands. BCI research studies demonstrated encouraging results in different areas such as neurorehabilitation, control of ar…
View article: Perceptual and Semantic Representations at Encoding Contribute to True and False Recognition of Objects
Perceptual and Semantic Representations at Encoding Contribute to True and False Recognition of Objects Open
When encoding new episodic memories, visual and semantic processing is proposed to make distinct contributions to accurate memory and memory distortions. Here, we used fMRI and preregistered representational similarity analysis to uncover …
View article: Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis
Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis Open
Background Semantic textual similarity (STS) is a natural language processing (NLP) task that involves assigning a similarity score to 2 snippets of text based on their meaning. This task is particularly difficult in the domain of clinical…
View article: Perceptual and Semantic Representations at Encoding Contribute to True and False Recognition of Objects
Perceptual and Semantic Representations at Encoding Contribute to True and False Recognition of Objects Open
When encoding new episodic memories, visual and semantic processing are proposed to make distinct contributions to accurate memory and memory distortions. Here, we used functional magnetic resonance imaging (fMRI) and representational simi…
View article: Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models
Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models Open
In this paper, we perform a systematic analysis of how closely the intermediate layers from LSTM and trans former language models correspond to human semantic knowledge. Furthermore, in order to make more meaningful comparisons with theori…
View article: The identification of mild cognitive impairment in Parkinson’s disease using EEG and machine learning
The identification of mild cognitive impairment in Parkinson’s disease using EEG and machine learning Open
Background Electroencephalography (EEG) is an inexpensive, non‐invasive and faster method to assess cognition in aging clinical groups. In this study, we are investigating the feasibility of using a ‘dry‐EEG’ mobile headset to assess cogni…
View article: Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis (Preprint)
Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis (Preprint) Open
BACKGROUND Semantic textual similarity (STS) is a natural language processing (NLP) task that involves assigning a similarity score to 2 snippets of text based on their meaning. This task is particularly difficult in the domain of clinica…
View article: Phexpo: a package for bidirectional enrichment analysis of phenotypes and chemicals
Phexpo: a package for bidirectional enrichment analysis of phenotypes and chemicals Open
Phenotypes are the result of the complex interplay between environmental and genetic factors. To better understand the interactions between chemical compounds and human phenotypes, and further exposome research we have developed “phexpo,” …
View article: Expanding the Vocabulary of a Protein: Application of Subword Algorithms to Protein Sequence Modelling
Expanding the Vocabulary of a Protein: Application of Subword Algorithms to Protein Sequence Modelling Open
Deep learning has proven to be a useful tool for modelling protein properties. However, given the variability in the length of proteins, it can be difficult to summarise the sequence of amino acids effectively. In many cases, as a result o…