1 citations · 2 across the 8 of their papers we have counts for
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Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning
Zhiming Xu, Huiyu Yi, Zhen-Hao Xie +4
Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks. However, adapti…
AffineLens: Capturing the Continuous Piecewise Affine Functions of Neural Networks
Yi Wei, Xuan Qi, Furao Shen +3
Piecewise affine neural networks (PANNs) provide a principled geometric perspective on neural network expressivity by characterizing the input--output map as a continuous piecewise…
Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals
Zhiming Xu, Baile Xu, Jian Zhao +2
Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplor…
Dual Prototypes for Adaptive Pre-Trained Model in Class-Incremental Learning
Zhiming Xu, Suorong Yang, Baile Xu +2
Class-incremental learning (CIL) aims to learn new classes while retaining previous knowledge. Although pre-trained model (PTM) based approaches show strong performance, directly f…
RAM: Replace Attention with MLP for Efficient Multivariate Time Series Forecasting
Suhan Guo, Jiahong Deng, Yi Wei +3
Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF). Yet, our unders…