Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Online updating of time series forecasting models aims to address the concept drifting
problem by efficiently updating forecasting models based on streaming data. Many …

Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …

Many recent machine learning tasks focus to develop models that can generalize to unseen
distributions. Domain generalization (DG) has become one of the key topics in various fields …

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed
for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic …

Due to privacy concerns and data transmission issues, Source-free Unsupervised Domain
Adaptation (SFDA) has gained popularity. It exploits pre-trained source models, rather than …

Since real-world machine systems are running in non-stationary environments, Continual
Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually …

The development of large vision-language models notably CLIP has catalyzed research into
effective adaptation techniques with a particular focus on soft prompt tuning. Conjointly test …

Despite their improved capabilities in generation and reasoning, adapting large language
models (LLMs) to the biomedical domain remains challenging due to their immense size …

Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously.
A recent development known as task arithmetic has revealed that several models, each fine …