20 citations · 25 across the 7 of their papers we have counts for
8 papers
Improving In-Context Learning with Small Language Model Ensembles
M. Mehdi Mojarradi, Lingyi Yang, Robert McCraith +1
Large language models (LLMs) have shown impressive capabilities across various tasks, but their performance on domain-specific tasks remains limited. While methods like retrieval a…
Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering
Yushi Yang, Andrew M. Bean, Robert McCraith +1
Fine-tuning Large Language Models (LLMs) incurs considerable training costs, driving the need for data-efficient training with optimised data ordering. Human-inspired strategies of…
Do Large Language Models have Shared Weaknesses in Medical Question Answering?
Andrew M. Bean, Karolina Korgul, Felix Krones +2
Large language models (LLMs) have made rapid improvement on medical benchmarks, but their unreliability remains a persistent challenge for safe real-world uses. To design for the u…
Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views
Robert McCraith, Eldar Insafutdinov, Lukas Neumann +1
We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects. Because the LiDAR point clouds are par…
Real Time Monocular Vehicle Velocity Estimation using Synthetic Data
Robert McCraith, Lukas Neumann, Andrea Vedaldi
Vision is one of the primary sensing modalities in autonomous driving. In this paper we look at the problem of estimating the velocity of road vehicles from a camera mounted on a m…
Calibrating Self-supervised Monocular Depth Estimation
Robert McCraith, Lukas Neumann, Andrea Vedaldi
In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training s…