Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Datasets with significant proportions of noisy (incorrect) class labels present challenges for
training accurate Deep Neural Networks (DNNs). We propose a new perspective for …

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables
clients to jointly train a global model. In real-world FL implementations, client data could …

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory
approximate nearest neighbor algorithms. It provides a standard interface for measuring the …

The geometric properties of contextual embedding spaces for deep language models such
as BERT and ERNIE, have attracted considerable attention in recent years. Investigations on …

With the continued digitization of societal processes, we are seeing an explosion in
available data. This is referred to as big data. In a research setting, three aspects of the data …

This paper is concerned with the estimation of a local measure of intrinsic dimensionality
(ID) recently proposed by Houle. The local model can be regarded as an extension of …

Researchers have long considered the analysis of similarity applications in terms of the
intrinsic dimensionality (ID) of the data. This theory paper is concerned with a generalization …

Kernel density estimation (KDE) is a popular nonparametric density estimation method. We
(1) derive finite-sample high-probability density estimation bounds for multivariate KDE …