Topological Data Analysis (TDA) is a recent and fast growing field providing a set of new
topological and geometric tools to infer relevant features for possibly complex data. This …

Topological data analysis (TDA) can broadly be described as a collection of data analysis
methods that find structure in data. These methods include clustering, manifold estimation …

The medial entorhinal cortex is part of a neural system for mapping the position of an
individual within a physical environment. Grid cells, a key component of this system, fire in a …

We address the problem of crowd localization, ie, the prediction of dots corresponding to
people in a crowded scene. Due to various challenges, a localization method is prone to …

Noisy labels can impair the performance of deep neural networks. To tackle this problem, in
this paper, we propose a new method for filtering label noise. Unlike most existing methods …

Although deep learning approaches have had tremendous success in image, video and
audio processing, computer vision, and speech recognition, their applications to three …

Inferring topological and geometrical information from data can offer an alternative
perspective in machine learning problems. Methods from topological data analysis, eg …

Topological data analysis offers a rich source of valuable information to study vision
problems. Yet, so far we lack a theoretically sound connection to popular kernel-based …

Detecting meaningful structure in neural activity and connectivity data is challenging in the
presence of hidden nonlinearities, where traditional eigenvalue-based methods may be …

Our intention, at the beginning, was to write a short paper resolving some technical issues in
the theory of topological persistence. Specifically, we wished to present a clean easy-to-use …