7 citations · 9 across the 11 of their papers we have counts for
11 papers
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
Wei Tang, Jinpei Han, Kangning Cui +10
Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a range of clinical applications.…
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3
Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response a…
Entropy Alone is Insufficient for Safe Selective Prediction in LLMs
Edward Phillips, Fredrik K. Gustafsson, Sean Wu +2
Selective prediction systems can mitigate harms resulting from language model hallucinations by abstaining from answering in high-risk cases. Uncertainty quantification techniques…
Democratising Clinical AI through Dataset Condensation for Classical Clinical Models
Anshul Thakur, Soheila Molaei, Pafue Christy Nganjimi +5
Dataset condensation (DC) learns a compact synthetic dataset that enables models to match the performance of full-data training, prioritising utility over distributional fidelity.…
Improving Clinical Dataset Condensation with Mode Connectivity-based Trajectory Surrogates
Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave +3
Dataset condensation (DC) enables the creation of compact, privacy-preserving synthetic datasets that can match the utility of real patient records, supporting democratised access…
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei +3
Large language models are known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy t…
Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
Archie Licudi, Anshul Thakur, Soheila Molaei +2
Asynchronous federated learning (FL) with heterogeneous clients faces two key issues: curvature-induced loss barriers encountered by standard linear parameter interpolation techniq…
RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction
Fenglin Liu, Jinge Wu, Hongjian Zhou +9
Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decisi…
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis
Anshul Thakur, Yichen Huang, Soheila Molaei +2
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from…
FOCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics
Pramit Saha, Felix Wagner, Divyanshu Mishra +5
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (P…
Large Language Models in the Clinic: A Comprehensive Benchmark
Fenglin Liu, Zheng Li, Hongjian Zhou +16
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with…