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 …

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to
a false sense of security in defenses against adversarial examples. While defenses that …

Deep Neural Networks (DNNs) have recently been shown to be vulnerable against
adversarial examples, which are carefully crafted instances that can mislead DNNs to make …

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 …

Deep neural networks progressively transform their inputs across multiple processing layers.
What are the geometrical properties of the representations learned by these networks? Here …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

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 …

Diffusion models recently have been successfully applied for the visual synthesis of
strikingly realistic appearing images. This raises strong concerns about their potential for …

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 …