Search Results for author:
Found 7 papers, 3 papers with code
Local Random Feature Approximations of the Gaussian Kernel
A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations.
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel
Commonly used approaches avoid computing the high-dimensional tensor product explicitly, resulting in a suboptimal dependence of $\mathcal{O}(3^p)$ in the embedding dimension.
Improved Random Features for Dot Product Kernels
These variance formulas elucidate conditions under which certain approximations (e. g., TensorSRHT) achieve lower variances than others (e. g., Rademacher sketches), and conditions under which the use of complex features leads to lower variances than real features.
Transfer Learning for Brain Tumor Segmentation
Therefore, there is a substantial demand for automatic image segmentation algorithms that produce a reliable and accurate segmentation of various brain tissue types.
Kernel computations from large-scale random features obtained by Optical Processing Units
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation.
Ambient Search: A Document Retrieval System for Speech Streams
We present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input.
Demonstrating Ambient Search: Implicit Document Retrieval for Speech Streams
In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input.
