This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural
Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel …

Deep-learning algorithms hold promise in processing physiological signal data, including
electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often …

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

Foundation models, especially LLMs, are profoundly transforming deep learning. Instead of
training many task-specific models, we can adapt a single pretrained model to many tasks …

This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network
designed to serve as a general-purpose model for time series analysis. The key design of …

In order to spot trends in the methodologies and procedures employed, this systematic
literature review will look at works on time series categorization. Six research questions are …

Time series remains one of the most challenging modalities in machine learning research.
Out-of-distribution (OOD) detection and generalization on time series often face difficulties …

Shapelets and CNN are two typical approaches to model time series. Shapelets aim at
finding a set of sub-sequences that extract feature-based interpretable shapes, but may …

While deep learning has demonstrated impressive progress, it remains a daunting
challenge to learn from hard samples as these samples are usually noisy and intricate …

Time series classification assigns predefined labels or classes to sequences of data points
ordered chronologically, which is a fundamental task for time series analysis. Existing time …