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Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution
Longquan Dai, Liang Tang, Yuan Xie, Jinhui Tang
Neural Information Processing Systems http://nips.cc/
AMOS: ALarge-ScaleAbdominalMulti-Organ BenchmarkforVersatileMedicalImageSegmentation
The significant heterogeneity poses higher requirements foralgorithm robustness.
the Ground up--Hierarchical Representation Learning by Chunking Shuchen Wu
Of course, the count function should be fixed.
But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun, Anna Gilbert, Ambuj Tewari
The random features method, proposed by Rahimi and Recht [2008], maps the data to a finite dimensional feature space as a random approximation to the feature space of RBF kernels. With explicit finite dimensional feature vectors available, the original KSVM is converted to a linear support vector machine (LSVM), that can be trained by faster algorithms (Shalev-Shwartz et al.
Coresets for Archetypal Analysis
Several approaches have been proposed to remedy the edacious nature of archetypal analysis, proposing, e.g.,efficient active-set quadratic programming (Chen etal.,2014),
