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Smaller, more accurate regression forests using tree alternating optimization
We show that using TAO with the bagging approach produces much better forests than random forests, Adaboost or gradient boosting in every dataset we have tried across a wide range of input and output dimensionality and sample size.
A Flexible, Extensible Software Framework for Neural Net Compression
We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).
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