Given only information in the form of similarity triplets Object A is more similar to object B than to object C about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similarity triplets, we aim at defining kernel functions that correspond to high-dimensional embeddings. These kernel functions can subsequently be used to apply any kernel method to the data set.

Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).

In this paper we present a novel method for efficient and effective 3D surface reconstruction in open scenes.

Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters.

Distributed representations provide a vector space that captures meaningful relationships between data instances.

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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.

More precisely, we study a Nyström approach to kernel k-means. Weanalyze thestatistical properties oftheproposed method andshow that it achieves the same accuracy of exact kernel k-means with only a fraction of computations.