5 citations · 7 across the 27 of their papers we have counts for
5 papers · 1 filter
Quantum Incremental Learning with Mixed State Prototypes
Yu Wu, Qianli Zhou, Xinyang Deng +3
Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints. In the Noisy Int…
EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling
Qian Yin, Jiaxing Li, Jiaqi Cheng +23
Earth observation satellite imaging scheduling is a challenging NP-hard combinatorial optimisation problem central to space mission operations. While next-generation agile Earth ob…
Informed Machine Learning with Knowledge Landmarks
Chuyi Dai, Witold Pedrycz, Suping Xu +2
Informed Machine Learning has emerged as a viable generalization of Machine Learning (ML) by building a unified conceptual and algorithmic setting for constructing models on a unif…
Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction
Guangyin Jin, Xiaohan Ni, Yanjie Song +4
With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-valu…
Theoretical Convergence of SMOTE-Generated Samples
Firuz Kamalov, Hana Sulieman, Witold Pedrycz
Imbalanced data affects a wide range of machine learning applications, from healthcare to network security. As SMOTE is one of the most popular approaches to addressing this issue,…