5 accepted papers
Federated Graph Learning enables multiple clients to collaboratively train graph models while protecting local private data. However, most studies have assumed that all clients contribute data voluntarily and actively. Without reasonable incentives, clients are often reluctant to contribute personal
Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. We tackle this problem with Reparameterized Orthogonal Equivalence Training (POET), a spectrum-preserving framework that optimizes each weight matrix through orthogonal equivale…
The rapid development of the Internet of Things (IoT) has resulted in an increasing volume of data, much of which contains sensitive private information. Federated Learning (FL) allows clients to train models without sharing raw data, showing significant potential for privacy protection. However, de…
Recently there has been significant progress in image captioning with the help of deep learning. However, captions generated by current state-of-the-art models are still far from satisfactory, despite high scores in terms of conventional metrics such as BLEU and CIDEr. Human-written captions are div…
Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks renders the ability to fit complex functions and the strong representation power to handle challenging tasks, it also lead…