University of Washington
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Butterfly Transform: An Efficient FFT Based Neural Architecture Design
- Keivan Alizadeh-Vahid, Ali Farhadi, Mohammad Rastegari
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 5 June 2019
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
- Seyed Abbas Hosseini, Ali Khodadadi, H. Rabiee
- Computer ScienceIEEE Transactions on Knowledge and Data…
- 4 March 2017
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
- Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, Ali Farhadi
- Computer ScienceArXiv
- 6 July 2020
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.