In recent years, significant progress has been made in develo** more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …

The key component in forecasting demand and consumption of resources in a supply
network is an accurate prediction of real-valued time series. Indeed, both service …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Synthetic aperture radar (SAR) satellites can provide microwave remote sensing images
without weather and light constraints, so they are widely applied in the maritime monitoring …

Fine-tuning pre-trained large language models in a parameter-efficient manner is widely
studied for its effectiveness and efficiency. The popular method of low-rank adaptation …

Inspired by the life philosophy, an ingenious gate (membership) function, which can mimic
the open and close of the gate in the real world, is proposed to realize feature selection (FS) …

The deployment of deep convolutional neural networks (CNNs) in many real world
applications is largely hindered by their high computational cost. In this paper, we propose a …

We explore a recently proposed Variational Dropout technique that provided an elegant
Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case …

Compression and computational efficiency in deep learning have become a problem of
great significance. In this work, we argue that the most principled and effective way to attack …

Recent research has demonstrated that feature attribution methods for deep networks can
themselves be incorporated into training; these attribution priors optimize for a model whose …