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Explore advanced graph learning techniques using subgraph-based networks for expressive, efficient, and domain-independent applications in data science and signal processing.
Explore graph learning techniques for gene regulatory network inference, focusing on single-view and multi-view approaches, their applications, and computational challenges in bioinformatics.
Explore graph constructions for machine learning, covering signal variation, sampling, and semisupervised learning. Gain insights into theoretical analysis and applications in deep learning geometry.
Explore how Graph Neural Networks function as dynamic programmers, bridging classical algorithms with neural approaches for optimal path finding and algorithmic reasoning.
Learn how IEEE HAC/SIGHT offers high-impact humanitarian engagement opportunities, advancing technologies for marginalized communities worldwide. Discover achievements, benefits, collaborations, and ways to get involved in this impactful initiative.
Explore graph theory, convergence, and transferability in large networks. Learn about graph neural networks, filters, and applications in multi-robot systems and signal processing.
Explore techniques for detecting and analyzing disinformation spread on social networks using causal inference, focusing on network construction, influence scoring, and bot detection.
Explore graph-based machine learning for modeling physical structures and dynamics, covering algorithms, architectures, simulations, and applications in various domains.
Explore model-based deep learning, deep equilibrium models, and their applications in image recovery, focusing on robustness, safety, and efficiency in undersampled data scenarios.
Explore cutting-edge signal processing techniques for image restoration, noise removal, and low-light enhancement in this comprehensive webinar series.
Explore computational imaging techniques, including mask-based imaging and lensless imaging, with Prof. Salman Asif. Learn about depth reconstruction, simulation results, and practical applications.
Explore computational imaging systems, diffractive optics, and neural network-based reconstruction in this advanced signal processing webinar by Prof. Wolfgang Heidrich from KAUST.
Prof. Bin Dong explores signal processing challenges, focusing on image reconstruction, deep learning applications, and task-driven approaches in computer vision and medical imaging.
Explore advanced ultrasound techniques and elastography methods with Prof. Doyley, covering palpation, harmonic imaging, and stress analysis for medical applications.
Explore advanced computational microscopy techniques using LED arrays for multimodal imaging, 3D phase reconstruction, and deep learning applications in high-resolution biological sample analysis.
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