Elon Musk doubles down on Grok Imagine after OpenAI's Sora shutdown: ‘Future of AI is…’ - Mint
<a href="https://news.google.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?oc=5" target="_blank">Elon Musk doubles down on Grok Imagine after OpenAI's Sora shutdown: ‘Future of AI is…’</a> <font color="#6f6
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The Rashomon Effect for Visualizing High-Dimensional Data
arXiv:2604.00485v1 Announce Type: new Abstract: Dimension reduction (DR) is inherently non-unique: multiple embeddings can preserve the structure of high-dimensional data equally well while differing in layout or geometry. In this paper, we formally define the Rashomon set for DR -- the collection of `good' embedding -- and show how embracing this multiplicity leads to more powerful and trustworthy representations. Specifically, we pursue three goals. First, we introduce PCA-informed alignment to steer embeddings toward principal components, making axes interpretable without distorting local neighborhoods. Second, we design concept-alignment regularization that aligns an embedding dimension with external knowledge, such as class labels or user-defined concepts. Third, we propose a method t
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