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Finally, a rate-distortion framework that accounts for the messy realities of scientific data compression: heterogeneity, finite lattices, and tiling constraints.
Learning recurrent dynamics in RNNs can hurt performance in real-time settings: fixing the poles leads to faster convergence and better results when data is limited.
