5 papers
Do Neural Networks Learn Structure-Preserving Maps? A Case Study in Latent-to-Hilbert Embeddings
Muhammad Adnan Shahzad
We ask whether a neural network can learn a structure-preserving map from a compressed latent space to a Hilbert-space representation. Using an 8-dimensional autoencoder bottleneck…
Hybrid Quantum-Classical Model for Image Classification
Muhammad Adnan Shahzad
This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10…
GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification
Yang Mu, Zhitong Xiong, Yi Wang +5
Global tree species mapping using remote sensing data is vital for biodiversity monitoring, forest management, and ecological research. However, progress in this field has been con…
MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification
Yang Mu, Muhammad Shahzad, Xiao Xiang Zhu
Multivariate Time Series Classification (MTSC) is crucial in extensive practical applications, such as environmental monitoring, medical EEG analysis, and action recognition. Real-…
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning
Yuanyuan Wang, Qian Song, Dawood Wasif +4
Uncertainty quantification (UQ) is essential for assessing the reliability of Earth observation (EO) products. However, the extensive use of machine learning models in EO introduce…