Search Results for author: Akshay Gadde

Found 7 papers, 0 papers with code

Rate distortion optimization over large scale video corpus with machine learning

no code implementations27 Aug 2020 Sam John, Akshay Gadde, Balu Adsumilli

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

no code implementations2 Feb 2017 Andrew Knyazev, Akshay Gadde, Hassan Mansour, Dong Tian

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

no code implementations18 May 2016 Eyal En Gad, Akshay Gadde, A. Salman Avestimehr, Antonio Ortega

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

Active Learning for Community Detection in Stochastic Block Models

no code implementations8 May 2016 Akshay Gadde, Eyal En Gad, Salman Avestimehr, Antonio Ortega

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 $D(a, b)<1$.

Active Learning Community Detection +1

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

no code implementations16 May 2014 Akshay Gadde, Aamir Anis, Antonio Ortega

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.

Active Learning

Localized Iterative Methods for Interpolation in Graph Structured Data

no code implementations9 Oct 2013 Sunil K. Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega

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.

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