13 citations · 17 across the 4 of their papers we have counts for
5 papers
Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer +3
Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.…
EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning
Shiming Chen, Shihuang Chen, Wenjin Hou +2
Zero-shot learning (ZSL) aims to recognize the novel classes which cannot be collected for training a prediction model. Accordingly, generative models (e.g., generative adversarial…
Assessor-Guided Learning for Continual Environments
Muhammad Anwar Ma'sum, Mahardhika Pratama, Edwin Lughofer +2
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pac…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Efficient Evaluation Methods for Neural Architecture Search: A Survey
Xiaotian Song, Xiangning Xie, Zeqiong Lv +4
Neural Architecture Search (NAS) has received increasing attention because of its exceptional merits in automating the design of Deep Neural Network (DNN) architectures. However, t…