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Butterfly Transform: An Efficient FFT Based Neural Architecture Design
It is shown that extending the butterfly operations from the FFT algorithm to a general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for CNN designs, and ShuffleNet-V2+BFT outperforms state-of-the-art architecture search methods MNasNet, FBNet and MobilenetV3 in the low FLOP regime.
Recurrent Poisson Factorization for Temporal Recommendation
Recurrent Poisson Factorization (RPF) framework is introduced that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback and demonstrates RPF's superior performance over many state-of-the-art methods on synthetic dataset, and wide variety of large scale real-world datasets.
In the Wild: From ML Models to Pragmatic ML Systems
A unified learning & evaluation framework - iN thE wilD (NED) is introduced, designed to be a more general paradigm by loosening the restrictive design decisions of past settings & imposing fewer restrictions on learning algorithms.