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Multi task learning
4.0
Source code file & quot; function for hashing large-scale multi task learning & quot;. Empirical evidence shows that hash is an effective strategy for dimension reduction and practical nonparametric estimation. We provide exponential tail boundary finite element - true hashing and show that the interaction between random subspaces is highly probabilistic and negligible. We demonstrate the feasibility of this approach with experimental results for a new use case - multi task learning with hundreds of thousands of tasks.
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