7 papers
Twin: Tuning Learning Rate and Weight Decay of Deep Homogeneous Classifiers without Validation
Lorenzo Brigato, Stavroula Mougiakakou
We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminati…
There are no Champions in Supervised Long-Term Time Series Forecasting
Lorenzo Brigato, Rafael Morand, Knut Strømmen +3
Recent advances in long-term time series forecasting have introduced numerous complex supervised prediction models that consistently outperform previously published architectures.…
Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis
Ethan Dack, Lorenzo Brigato, Vasilis Dedousis +9
Masked autoencoders (MAEs) have emerged as a powerful approach for pre-training on unlabelled data, capable of learning robust and informative feature representations. This is part…
Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment
Maria Panagiotou, Lorenzo Brigato, Vivien Streit +13
Despite recent advances in insulin preparations and technology, adjusting insulin remains an ongoing challenge for the majority of people with type 1 diabetes (T1D) and longstandin…
The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes
Maria Panagiotou, Knut Stroemmen, Lorenzo Brigato +2
The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have advanced significantly, with artific…
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition
Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato +1
Food recognition models often struggle to distinguish between seen and unseen samples, frequently misclassifying samples from unseen categories by assigning them an in-distribution…