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Improved Generalization Bound for Deep Neural Networks Using Geometric Functional Analysis
Understanding how a neural network behaves in multiple domains is the key to further its explainability, generalizability, and robustness.
$Graph Embedding via Topology and Functional Analysis$
Graphs have been ubiquitous in Machine Learning due to their versatile nature in modelling real world situations . Graph embedding is an important precursor to using graphs in Machine Learning , and much of performance of algorithms developed later depends heavily on this.
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