Search Results for author:
Found 7 papers, 1 papers with code
Rate distortion optimization over large scale video corpus with machine learning
We present an efficient codec-agnostic method for bitrate allocation over a large scale video corpus with the goal of minimizing the average bitrate subject to constraints on average and minimum quality.
Guided Signal Reconstruction Theory
New frame-less reconstruction methods are proposed, based on a novel concept of a reconstruction set, defined as a shortest pathway between the sample consistent set and the guiding set.
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches
A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggressive search for the boundary of the signal over the graph.
Active Learning for Community Detection in Stochastic Block Models
Our main result is to show that, under certain conditions, sampling the labels of a vanishingly small fraction of nodes (a number sub-linear in $n$) is sufficient for exact community detection even when $D(a, b)<1$.
A Probabilistic Interpretation of Sampling Theory of Graph Signals
We give a probabilistic interpretation of sampling theory of graph signals.
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals
The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices.
Localized Iterative Methods for Interpolation in Graph Structured Data
In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples.
