McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
Eidgenössische Technische Hochschule Zürich（苏黎世联邦理工学院）；Carnegie Mellon（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；City University of Hong Kong（香港城市大学）；Amazon（亚马逊）；Google（谷歌）；Facebook（脸书）
J. D. Curtó, I. C. Zarza, Feng Yang, Alex Smola, Fernando de la Torre, Chong Wah Ngo, Luc van Gool
McKernel引入了一个框架，在小批量设置中使用内核近似，并使用随机梯度下降（SGD）作为深度学习的替代方案。基于Random Kitchen Sinks [Rahimi and Recht 2007]，我们为大规模机器学习提供了一个C++库。它包含[Le et al. 2013]中算法的CPU优化实现，允许在对数线性时间内计算近似的内核扩展。该算法需要计算Walsh Hadamard矩阵的乘积。一个高速缓存友好的快速沃尔什哈达玛，实现引人注目的速度和优于目前国家的最先进的方法已经开发出来。McKernel建立了一种新的学习架构的基础，该架构允许获得结合闪电内核扩展和线性分类器的大规模非线性分类。它在小批量环境中的工作类似于神经网络。我们通过对MNIST和FASHION MNIST的广泛实验证明了我们方法的有效性[Xiao et al. 2017]。
英文摘要：
McKernel introduces a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. Based on Random Kitchen Sinks [Rahimi and Recht 2007], we provide a C++ library for Large-scale Machine Learning. It contains a CPU optimized implementation of the algorithm in [Le et al. 2013], that allows the computation of approximated kernel expansions in log-linear time. The algorithm requires to compute the product of matrices Walsh Hadamard. A cache friendly Fast Walsh Hadamard that achieves compelling speed and outperforms current state-of-the-art methods has been developed. McKernel establishes the foundation of a new architecture of learning that allows to obtain large-scale non-linear classification combining lightning kernel expansions and a linear classifier. It travails in the mini-batch setting working analogously to Neural Networks. We show the validity of our method through extensive experiments on MNIST and FASHION MNIST [Xiao et al. 2017].
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
University of Iowa（爱荷华大学）；University of Minnesota（明尼苏达大学）；Individual Researcher（独立研究者）；AIScientists, Inc. (MorphMind)（AIScientists公司（MorphMind））；Department of Statistics and Actuarial Science, University of Iowa（爱荷华大学统计与精算科学系）
UPMC Université Paris 6, Sorbonne Universités（巴黎第六大学（UPMC），索邦大学集团）；École Polytechnique（巴黎综合理工学院）；Athens University of Economics and Business（雅典经济与商业大学）
Kernel methods through the roof: handling billions of points efficiently
Università degli Studi di Genova（热那亚大学）；Center for Brains, Minds and Machines, MIT（麻省理工学院大脑、心智与机器中心）；Istituto Italiano di Tecnologia（意大利理工学院）；INRIA - Département d’Informatique de l’École Normale Supérieure - PSL Research University（法国国家信息与自动化研究所 - 巴黎高等师范学院计算机系 - PSL研究大学）
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Federal University of São Carlos（圣卡洛斯联邦大学）；Universidade de São Paulo（圣保罗大学）；Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo（圣保罗大学数学与计算机科学学院）
Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature
Lattice Lab, Toyota Motor Corporation / Machine Learning Research Group, University of Oxford（丰田公司 lattice 实验室 / 机器学习研究组，牛津大学）；The University of Osaka / Lattice Lab, Toyota Motor Corporation / RIKEN AIP（大阪大学 / 丰田公司 lattice 实验室 / RIKEN AIP）；Machine Learning Research Group, University of Oxford（机器学习研究组，牛津大学）
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
CSML Unit, Italian Institute of Technology（意大利理工学院CSML单位）；CMAP, École Polytechnique（巴黎政治学院CMAP单位）；Centre Inria de l’Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学INRIA中心）；Paris Dauphine University（巴黎政治学院）；Faculty of Science, University of Novi Sad（诺维萨德大学科学学院）；Dept. of Computer Science, University College London（伦敦大学学院计算机科学系）
