Pretrained Multimodal Large Language Models (MLLMs) are increasingly deployed in medical AI systems for clinical reasoning, diagnosis support, and report genera...
Announcement I’m happy to share something special today: my new book, _Building A Small Language Model from Scratch: A Practical Guide_, is now available on Am...
We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making ...
Learning models over factorized joins avoids redundant computations by identifying and pre-computing shared cofactors. Previous work has investigated the perfor...
This chapter explores the application of Large Language Models in the legal domain, showcasing their potential to optimise and augment traditional legal tasks b...
As a machine learning practitioner, it's essential to recognize that AI is not a replacement for human empathy but rather a partner in augmenting it. When devel...
DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point. The post The Machine Learning “Advent Calendar” Day 1...
This paper presents OnCoCo 1.0, a new public dataset for fine-grained message classification in online counseling. It is based on a new, integrative system of c...
Low-power microcontroller (MCU) hardware is currently evolving from single-core architectures to predominantly multi-core architectures. In parallel, new embedd...
The recent convergence of pervasive computing and machine learning has given rise to numerous services, impacting almost all areas of economic and social activi...
Constructing a Pareto set is pivotal for navigating the capability-efficiency trade-offs in Large Language Models (LLMs); however, existing merging techniques r...
Constructing a Pareto set is pivotal for navigating the capability-efficiency trade-offs in Large Language Models (LLMs); however, existing merging techniques r...
