The rapid growth of Large Language Models (LLMs) has been a driving force in
transforming various domains, reshaping the artificial general intelligence landscape …

DNN-based frame interpolation--that generates the intermediate frames given two
consecutive frames--typically relies on heavy model architectures with a huge number of …

The existing model compression methods via structured pruning typically require
complicated multi-stage procedures. Each individual stage necessitates numerous …

Large Language Models (LLMs) have transformed the landscape of artificial intelligence,
while their enormous size presents significant challenges in terms of computational costs …

Currently, one of the major challenges in deep learning-based video frame interpolation
(VFI) is the large model size and high computational complexity associated with many high …

Optimization problems with group sparse regularization are ubiquitous in various popular
downstream applications, such as feature selection and compression for Deep Neural …

DNN-based frame interpolation, which generates intermediate frames from two consecutive
frames, is often dependent on model architectures with a large number of features …

In the past few decades, with the large-scale application of deep learning technology, the
neural network inference speed problem is becoming more and more severe, especially in …

Neural network compression has been an increasingly important subject, not only due to its
practical relevance, but also due to its theoretical implications, as there is an explicit …

Compressing a predefined deep neural network (DNN) into a compact sub-network with
competitive performance is crucial in the efficient machine learning realm. This topic spans …