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…
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…
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…