In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

While collections of documents are often annotated with hierarchically structured concepts,
the benefits of these structures are rarely taken into account by classification techniques …

Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …

The problem of extracting continuous structures from noisy or cluttered images is a difficult
one. Successful extraction depends critically on the ability to balance prior constraints on …

Abstract The Average Mixing Kernel Signature is a novel spectral signature for points on non-
rigid three-dimensional shapes. It is based on a quantum exploration process of the shape …

Text Classification methods have been improving at an unparalleled speed in the last
decade thanks to the success brought about by deep learning. Historically, state-of-the-art …

Partial similarity problems arise in numerous applications that involve real data acquisition
by 3D sensors, inevitably leading to missing parts due to occlusions and partial views. In this …

Abstract We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for
points on non-rigid three-dimensional shapes based on the average mixing kernel and …

Functional representation is a well-established approach to represent dense
correspondences between deformable shapes. The approach provides an efficient low rank …

In this paper we develop an in‐depth theoretical investigation of the discrete Hamiltonian
eigenbasis, which remains quite unexplored in the geometry processing community. This …