4 papers
Robust Losses from Univariate Base Functions for Noisy-Label Learning
Peng Hu, Jianwei Ma
Learning with noisy labels is a fundamental problem in training reliable deep neural networks. Robust loss functions provide a direct and effective way to mitigate the adverse effe…
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
Feng-Lei Fan, Meng Wang, Hang-Cheng Dong +2
In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons. Recently, more and more deep learning studies have b…
Personalized Federated Learning via Gaussian Generative Modeling
Peng Hu, Jianwei Ma
Federated learning has emerged as a paradigm to train models collaboratively on inherently distributed client data while safeguarding privacy. In this context, personalized federat…
CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning
Zhenquan Yao, Zitong Huang, Yihan Zeng +5
Graphical User Interface (GUI) Agents, benefiting from recent advances in multimodal large language models (MLLM), have achieved significant development. However, due to the freque…