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Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
To this end, inspired by recent works on denoising and the success of the cosine-similarity-based objective functions in representation learning, we propose the denoising Cosine-Similarity (dCS) loss.
Equivariant online predictions of non-stationary time series
To analyze the theoretical predictive properties of statistical methods under this setting, we first define the Kullback-Leibler risk, in order to place the problem within a decision theoretic framework.
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