Search Results for author: David Clifton

Found 9 papers, 3 papers with code

Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks

no code implementations16 Feb 2024 Niall Taylor, Upamanyu Ghose, Omid Rohanian, Mohammadmahdi Nouriborji, Andrey Kormilitzin, David Clifton, Alejo Nevado-Holgado

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods.

Decision Making

Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression

1 code implementation SemEval (NAACL) 2022 Mohammadmahdi Nouriborji, Omid Rohanian, David Clifton

This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B.

Multi-Task Learning regression

Assessing the risk of re-identification arising from an attack on anonymised data

no code implementations31 Mar 2022 Anna Antoniou, Giacomo Dossena, Julia MacMillan, Steven Hamblin, David Clifton, Paula Petrone

The objective of this work is to calculate the risk of re-identification arising from a malicious attack to an anonymised dataset, as described below.


Privacy-aware Early Detection of COVID-19 through Adversarial Training

no code implementations9 Jan 2022 Omid Rohanian, Samaneh Kouchaki, Andrew Soltan, Jenny Yang, Morteza Rohanian, Yang Yang, David Clifton

One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.

Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals

1 code implementation19 Mar 2021 Dani Kiyasseh, Tingting Zhu, David Clifton

Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the form of a clinical report, a cumbersome process that is prone to errors.

Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location

no code implementations ICML 2020 Rasheed el-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton

Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department.

reinforcement-learning Reinforcement Learning (RL)

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