Defines the neural codebook channel K_{e→d}(j|i) and proves a Bernoulli-KL bound on encoder-decoder mismatch in VAEs that cannot be recovered from marginal histograms or mutual information.
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SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.
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Lost and Found in Translation: Variational Diagnostics for Neural Codebook Channels
Defines the neural codebook channel K_{e→d}(j|i) and proves a Bernoulli-KL bound on encoder-decoder mismatch in VAEs that cannot be recovered from marginal histograms or mutual information.
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SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.