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Explore geometric deep learning techniques for drug discovery, covering graph neural networks, molecule generation, and retrosynthetic prediction. Gain insights into cutting-edge research and applications in pharmaceutical development.
Explore real-world challenges in law enforcement image analysis, covering infrared footage, lens distortion, perspective issues, compression artifacts, and AI impacts on forensic video interpretation.
Explore brain imaging through graph signal processing, connecting anatomy and activity. Learn MRI techniques, brain graphs, and innovative analysis methods for neuroimaging data.
Explore techniques to enhance robustness of Graph Neural Networks, including adaptive attacks, certificates, and collective reasoning. Learn strategies to improve model resilience in graph-based machine learning.
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.
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