In this paper, we propose a new six-dimensional movable antenna (6DMA) system for future
wireless networks to improve the communication performance. Unlike the traditional fixed …

Fine-tuning is the primary methodology for tailoring pre-trained large language models to
specific tasks. As the model's scale and the diversity of tasks expand, parameter-efficient fine …

The purpose of this survey is to serve both as a gentle introduction and a coherent overview
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …

Network systems consist of subsystems and their interconnections and provide a powerful
framework for the analysis, modeling, and control of complex systems. However, subsystems …

Building useful predictive models often involves learning from sensitive data. Training
models with differential privacy can guarantee the privacy of such sensitive data. For convex …

This work considers the problem of computing distances between structured objects such as
undirected graphs, seen as probability distributions in a specific metric space. We consider a …

Recent empirical works show that large deep neural networks are often highly redundant
and one can find much smaller subnetworks without a significant drop of accuracy. However …

Training machine learning (ML) models with large datasets can incur significant resource
contention on shared clusters. This training typically involves many iterations that continually …

Classical optimal transport problem seeks a transportation map that preserves the total mass
between two probability distributions, requiring their masses to be equal. This may be too …

In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be
a very effective approach. Yet, relying solely on a single detector is also a major limitation …