5 accepted papers
Traditional Discriminant analysis (DA) is one of the classical supervised learning algorithms to reduce the dimensionality of data with Gaussian assumption. Since the unique class mean in traditional DA is intractable to estimate the non-Gaussian distrbution of data, some existing DA algorithms base
Fully Few-shot Class-incremental Audio Classification (FFCAC) is challenging since the training samples are limited both in the incremental sessions and in the base session. Existing few-shot learning methods suffer from catastrophic forgetting and overfitting when applied to FFCAC.Pre-trained Audio
In deep reinforcement learning (DRL), the presence of dormant neurons leads to a significant reduction in network capacity, which results in sub-optimal performance and limited sample efficiency. Existing training techniques, especially those relying on periodic resetting (PR), exacerbate this issue
Refining the path tracking of articulated steering vehicles amidst terrain disturbances presents a formidable challenge. While model predictive control (MPC) offers promise in tackling this issue, its efficacy is often hindered by model intricacies and inaccuracies. In this communication, an innovat
Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties in drawing accurate class prototypes from support set samples, primarily due to probable large intra-class differences a…