8 research outputs found
Learning inducing points and uncertainty on molecular data
Uncertainty control and scalability to large datasets are the two main issues
for the deployment of Gaussian process models into the autonomous material and
chemical space exploration pipelines. One way to address both of these issues
is by introducing the latent inducing variables and choosing the right
approximation for the marginal log-likelihood objective. Here, we show that
variational learning of the inducing points in the high-dimensional molecular
descriptor space significantly improves both the prediction quality and
uncertainty estimates on test configurations from a sample molecular dynamics
dataset. Additionally, we show that inducing points can learn to represent the
configurations of the molecules of different types that were not present within
the initialization set of inducing points. Among several evaluated approximate
marginal log-likelihood objectives, we show that the predictive log-likelihood
provides both the predictive quality comparable to the exact Gaussian process
model and excellent uncertainty control. Finally, we comment on whether a
machine learning model makes predictions by interpolating the molecular
configurations in high-dimensional descriptor space. We show that despite our
intuition, and even for densely sampled molecular dynamics datasets, most of
the predictions are done in the extrapolation regime.Comment: 8 pages, 5 figure
Signals on Graphs: Uncertainty Principle and Sampling
In many applications, the observations can be represented as a signal defined
over the vertices of a graph. The analysis of such signals requires the
extension of standard signal processing tools. In this work, first, we provide
a class of graph signals that are maximally concentrated on the graph domain
and on its dual. Then, building on this framework, we derive an uncertainty
principle for graph signals and illustrate the conditions for the recovery of
band-limited signals from a subset of samples. We show an interesting link
between uncertainty principle and sampling and propose alternative signal
recovery algorithms, including a generalization to frame-based reconstruction
methods. After showing that the performance of signal recovery algorithms is
significantly affected by the location of samples, we suggest and compare a few
alternative sampling strategies. Finally, we provide the conditions for perfect
recovery of a useful signal corrupted by sparse noise, showing that this
problem is also intrinsically related to vertex-frequency localization
properties.Comment: This article is the revised version submitted to the IEEE
Transactions on Signal Processing on May, 2016; first revision was submitted
on January, 2016; original manuscript was submitted on July, 2015. The work
includes 16 pages, 8 figure
An introduction to hypergraph signal processing
Developing tools to analyze signals defined over a graph is a research area that is attracting a significant amount of contributions because of its many applications. However, a graph representation does not capture the overall information about the data, as it implicitly takes into account only pairwise relations. The goal of this paper is to extend signal processing tools to signals defined over hypergraphs, which represent a formal framework to describe multi-way relations among the data. First, we suggest alternative ways to introduce a Fourier Transform (FT) for signals defined over hypergraphs and, in particular, for simplicial complexes. Then, building on the notion of Fourier Transform, we derive a sampling theorem aimed at identifying the minimum number of samples necessary to encode all information about band-limited hypergraph signals
On the degrees of freedom of signals on graphs
Continuous-time signals are well known for not being perfectly localized in both time and frequency domains. Conversely, a signal defined over the vertices of a graph can be perfectly localized in both vertex and frequency domains. We derive the conditions ensuring the validity of this property and then, building on this theory, we provide the conditions for perfect reconstruction of a graph signal from its samples. Next, we provide a finite step algorithm for the reconstruction of a band-limited signal from its samples and then we show the effect of sampling a non perfectly band-limited signal and show how to select the bandwidth that minimizes the mean square reconstruction error
Analytic signal in many dimensions
Submitted to Applied and Computational Harmonic AnalysisIn this paper we extend analytic signal to the multidimensional case. First it is shown how to obtain separate phase-shifted components and how to combine them into instantaneous amplitude and phase. Secondly we define the proper hypercomplex analytic signal as a holo-morphic hypercomplex function on the boundary of polydisk in the hypercomplex space. Next it is shown that the correct phase-shifted components can be obtained by positive frequency restriction of the Scheffers-Fourier transform based on the commutative and associative algebra generated by the set of elliptic hypercomplex numbers. Moreover we demonstrate that for d > 2 there is no corresponding Clifford-Fourier transform that allows to recover phase-shifted components correctly. Finally the euclidean-domain construction of instantaneous amplitude is extended to manifold and manifold-like graphs and point clouds
Analytic signal in many dimensions
Submitted to Applied and Computational Harmonic AnalysisIn this paper we extend analytic signal to the multidimensional case. First it is shown how to obtain separate phase-shifted components and how to combine them into instantaneous amplitude and phase. Secondly we define the proper hypercomplex analytic signal as a holo-morphic hypercomplex function on the boundary of polydisk in the hypercomplex space. Next it is shown that the correct phase-shifted components can be obtained by positive frequency restriction of the Scheffers-Fourier transform based on the commutative and associative algebra generated by the set of elliptic hypercomplex numbers. Moreover we demonstrate that for d > 2 there is no corresponding Clifford-Fourier transform that allows to recover phase-shifted components correctly. Finally the euclidean-domain construction of instantaneous amplitude is extended to manifold and manifold-like graphs and point clouds
Multidimensional analytic signal with application on graphs
International audienceIn this work we provide an extension to analytic signal method for multidimensional signals. First, expressions for separate phase-shifted components are given. Second, we show that phase-shifted components could be obtained from hypercomplex Fourier transform in the algebra of commuta-tive hypercomplex numbers. Finally we apply multidimen-sional analytic signal method to signals defined on graphs