24 citations · 25 across the 4 of their papers we have counts for
5 papers
Local Random Feature Approximations of the Gaussian Kernel
Jonas Wacker, Maurizio Filippone
A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations. In this work, we focus on the…
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel
Jonas Wacker, Ruben Ohana, Maurizio Filippone
Randomized sketches of a tensor product of vectors follow a tradeoff between statistical efficiency and computational acceleration. Commonly used approaches avoid computing the…
Improved Random Features for Dot Product Kernels
Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone
Dot product kernels, such as polynomial and exponential (softmax) kernels, are among the most widely used kernels in machine learning, as they enable modeling the interactions betw…
Kernel computations from large-scale random features obtained by Optical Processing Units
Ruben Ohana, Jonas Wacker, Jonathan Dong +4
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation. However, performing random projection…
Transfer Learning for Brain Tumor Segmentation
Jonas Wacker, Marcelo Ladeira, José Eduardo Vaz Nascimento
Gliomas are the most common malignant brain tumors that are treated with chemoradiotherapy and surgery. Magnetic Resonance Imaging (MRI) is used by radiotherapists to manually segm…