Enabling Seamless Neural Model Interoperability: A Novel Machine Learning Approach Through Relative Representations

In the cutting-edge sphere of machine learning, manipulating and comprehending data within vast, high-dimensional spaces are formidable challenges. At the heart of numerous applications, from the nuanced realms of image and text analysis to the intricate networks of graph-based tasks, lies the endeavor to distill the essence of data into latent representations. These representations aim […]

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