1 citations · 1 across the 5 of their papers we have counts for
6 papers
Progressive Multi-Ancestor Bit-Depth Distillation
Adil Mubashir Chaudhry, Osama Ahmad, Zubair Khalid +1
Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computational, memory, and energy reso…
Robust Spatiotemporal Forecasting Using Adaptive Deep-Unfolded Variational Mode Decomposition
Osama Ahmad, Lukas Wesemann, Fabian Waschkowski +1
Accurate spatiotemporal forecasting is critical for numerous complex systems but remains challenging due to complex volatility patterns and spectral entanglement in conventional gr…
Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data Using Variational Mode Graph Neural Networks with 3D Attention
Osama Ahmad, Zubair Khalid
This paper focuses on improving the robustness of spatiotemporal long-term prediction using a variational mode graph convolutional network (VMGCN) by introducing 3D channel attenti…
Spatiotemporal Air Quality Mapping in Urban Areas Using Sparse Sensor Data, Satellite Imagery, Meteorological Factors, and Spatial Features
Osama Ahmad, Zubair Khalid, Muhammad Tahir +1
Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and…
Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting
Osama Ahmad, Lukas Wesemann, Fabian Waschkowski +1
This paper focuses on spatiotemporal (ST) traffic prediction using graph neural networks (GNNs). Given that ST data comprises non-stationary and complex temporal patterns, interpre…
Mending of Spatio-Temporal Dependencies in Block Adjacency Matrix
Osama Ahmad, Omer Abdul Jalil, Usman Nazir +1
In the realm of applications where data dynamically evolves across spatial and temporal dimensions, Graph Neural Networks (GNNs) are often complemented by sequence modeling archite…