Person

Mohammadjavad Faraji

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Related publications (6)

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A Multiscale Pyramid Transform for Graph Signals

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weighted graph, as they do not capture the intrinsic topology of the graph data ...

Institute of Electrical and Electronics Engineers2016

Learning with Surprise

Mohammadjavad Faraji

Everybody knows what it feels to be surprised. Surprise raises our attention and is crucial for learning. It is a ubiquitous concept whose traces have been found in both neuroscience and machine learning. However, a comprehensive theory has not yet been de ...

EPFL2016

Balancing New Against Old Information: The Role of Surprise

Wulfram Gerstner, Kerstin Preuschoff, Mohammadjavad Faraji

Surprise is a widely used concept describing a range of phenomena from unexpected events to behavioral responses. We propose a measure of surprise, to arrive at a new framework for surprise-driven learning. There are two components to this framework: (i) a ...

Public Library of Science2016

A Laplacian pyramid scheme in graph signal processing

Mohammadjavad Faraji

In this report we extend the ideas behind classical multiscale signal processing techniques in order to analyze data residing on graphs. In particular, we extend the notions of filtering, downsampling, and upsampling to functions defined on graphs. We then ...

2011

Semi-Supervised Learning with Spectral Graph Wavelets

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...

2011

Constructing and decoding GWBE codes using Kronecker products

Mohammadjavad Faraji, Pedram Pad

In this letter, we introduce a novel method for constructing large size generalized Welch bound equality (GWBE) matrices. This method can also be used for the construction of large WBE matrices. The advantage of this method is its low complexity for constr ...

Institute of Electrical and Electronics Engineers2010