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Found 13 papers, 2 papers with code
Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accurately modeling complex distributions, but their training is known to be difficult due to instabilities caused by a challenging minimax optimization problem.
A domain agnostic measure for monitoring and evaluating GANs
Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training.
Improving DNN-based Music Source Separation using Phase Features
Music source separation with deep neural networks typically relies only on amplitude features.
Inpainting of long audio segments with similarity graphs
We present a novel method for the compensation of long duration data loss in audio signals, in particular music.
Predicting the evolution of stationary graph signals
An emerging way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph.
Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks.
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph.
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space.
Global and Local Uncertainty Principles for Signals on Graphs
Accordingly, we suggest a new way to incorporate a notion of locality, and develop local uncertainty principles that bound the concentration of the analysis coefficients of each atom of a localized graph spectral filter frame in terms of quantities that depend on the local structure of the graph around the center vertex of the given atom.
Compressive PCA for Low-Rank Matrices on Graphs
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements.
Accelerated filtering on graphs using Lanczos method
Signal-processing on graphs has developed into a very active field of research during the last decade.
Numerical Analysis
Fast Robust PCA on Graphs
Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models.
UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions.
