Search Results for author:
Found 6 papers, 4 papers with code
Generalizing across Temporal Domains with Koopman Operators
By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment.
Domain-Augmented Domain Adaptation
Unsupervised domain adaptation (UDA) enables knowledge transfer from the labelled source domain to the unlabeled target domain by reducing the cross-domain discrepancy.
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers.
TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition
TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously.
TSception: A Deep Learning Framework for Emotion Detection Using EEG
TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously.
