Muhammad Shahzad

Jinming Xing, Guoheng Sun, Hui Sun et al.

Spatial-temporal network traffic forecasting is a challenging task due to the complex spatial relationships and dynamic temporal patterns present in each node. Traditional regression methods are not directly applicable to such graph data. Recently, Graph Neural Networks (GNNs) have been widely used to model spatial-temporal dependencies. However, existing methods face several limitations: (1) They rely solely on a predefined spatial adjacency matrix, overlooking hidden low-level temporal information. (2) They model spatial and temporal information separately, which inevitably leads to a loss of joint dependencies, or they capture only global or local dependencies. To address these issues, we propose the \textbf{G}lobal-\textbf{L}ocal \textbf{S}patial-\textbf{T}emporal \textbf{a}ware \textbf{G}raph \textbf{AT}tention Network (GLSTaGAT). Specifically, we adopt a data-driven spatial-temporal fusion graph that incorporates low-level spatial and temporal information, serving as the foundation for further graph convolutions. The GLSTaGAT block and its pooling variant are proposed to simultaneously capture local and global spatial-temporal dependencies. Additionally, we introduce node normalization to mitigate covariance shifts, enabling a smoother training process. An encoder-only transformer is utilized to model high-level joint dependencies, and a multi-head attention prediction layer is designed for final information aggregation and prediction. Experimental results on real-world datasets demonstrate that GLSTaGAT outperforms the baselines by 32.14\% (MAE), 28.30\% (RMSE), and 20.47\% (SMAPE) on average.

Faraz Waseem, Muhammad Shahzad

An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significant progress in multimodal large language models (MLLMs), generating extended videos remains a formidable challenge. As of this writing, OpenAI's Sora, the current state-of-the-art system, is still limited to producing videos that are up to one minute in length. This limitation stems from the complexity of long video generation, which requires more than generative AI techniques for approximating density functions essential aspects such as planning, story development, and maintaining spatial and temporal consistency present additional hurdles. Integrating generative AI with a divide-and-conquer approach could improve scalability for longer videos while offering greater control. In this survey, we examine the current landscape of long video generation, covering foundational techniques like GANs and diffusion models, video generation strategies, large-scale training datasets, quality metrics for evaluating long videos, and future research areas to address the limitations of the existing video generation capabilities. We believe it would serve as a comprehensive foundation, offering extensive information to guide future advancements and research in the field of long video generation.

Alejandro Rodriguez Dominguez, Muhammad Shahzad, Xia Hong

Existing approaches to predictive uncertainty rely either on multi-hypothesis prediction, which promotes diversity but lacks principled aggregation, or on ensemble learning, which improves accuracy but rarely captures the structured ambiguity. This implicitly means that a unified framework consistent with the loss geometry remains absent. The Structured Basis Function Network addresses this gap by linking multi-hypothesis prediction and ensembling through centroidal aggregation induced by Bregman divergences. The formulation applies across regression and classification by aligning predictions with the geometry of the loss, and supports both a closed-form least-squares estimator and a gradient-based procedure for general objectives. A tunable diversity mechanism provides parametric control of the bias-variance-diversity trade-off, connecting multi-hypothesis generalisation with loss-aware ensemble aggregation. Experiments validate this relation and use the mechanism to study the complexity-capacity-diversity trade-off across datasets of increasing difficulty with deep-learning predictors.

Alejandro Rodriguez Dominguez, Muhammad Shahzad, Xia Hong

Multi-modal problems can be effectively addressed using multiple hypothesis frameworks, but integrating these frameworks into learning models poses significant challenges. This paper introduces a Structured Radial Basis Function Network (s-RBFN) as an ensemble of multiple hypothesis predictors for regression. During the training of the predictors, first the centroidal Voronoi tessellations are formed based on their losses and the true labels, representing geometrically the set of multiple hypotheses. Then, the trained predictors are used to compute a structured dataset with their predictions, including centers and scales for the basis functions. A radial basis function network, with each basis function focused on a particular hypothesis, is subsequently trained using this structured dataset for multiple hypotheses prediction. The s-RBFN is designed to train efficiently while controlling diversity in ensemble learning parametrically. The least-squares approach for training the structured ensemble model provides a closed-form solution for multiple hypotheses and structured predictions. During the formation of the structured dataset, a parameter is employed to avoid mode collapse by controlling tessellation shapes. This parameter provides a mechanism to balance diversity and generalization performance for the s-RBFN. The empirical validation on two multivariate prediction datasets-air quality and energy appliance predictions-demonstrates the superior generalization performance and computational efficiency of the structured ensemble model compared to other models and their single-hypothesis counterparts.