Machine learning

Machine learning has progressed significantly with the integration of Bayesian methods and innovative active learning strategies. Two research papers from the University of Copenhagen and the University of Oxford have laid substantial groundwork for further advancements in this area: The Danish researchers delved into ensemble strategies for deep neural networks, focusing on Bayesian and PAC-Bayesian (Probably…

Transformer-based Large Language Models (LLMs) have become essential to Natural Language Processing (NLP), with their self-attention mechanism delivering impressive results across various tasks. However, this mechanism struggles with long sequences, since the computational load and memory requirements increase dramatically based on sequence length. Alternatives have been sought to optimize the self-attention layers, but these often…

Data-driven techniques, such as imitation and offline reinforcement learning (RL), that convert offline datasets into policies are seen as solutions to control problems across many fields. However, recent research has suggested that merely increasing expert data and finetuning imitation learning can often surpass offline RL, even if RL has access to abundant data. This finding…

The field of radiology has seen a transformative impact with the advent of generative vision-language models (VLMs), automating medical image interpretation and report generation. This innovative tech has shown potential in reducing radiologists’ workload and improving diagnostic accuracy. However, a challenge to this technology is its propensity to produce hallucinated content — text that is…