Mohammad Javad Faraji

Learn More
—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 geometric structure of the underlying graph data domain. In this paper, we adapt the Laplacian pyramid transform for signals on(More)
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 spectral graph wavelets behave like discrete multiscale differential operators on graphs, and thus can(More)
this paper we wish to introduce a method to reconstruct large size Welch Bound Equality (WBE) codes from small size WBE codes. The advantage of these codes is that the implementation of ML decoder for the large size codes is reduced to implementation of ML decoder for the core codes. This leads to a drastic reduction of the computational cost of ML decoder.(More)
Surprise is a ubiquitous concept describing a wide 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 confidence-adjusted surprise measure to capture environmental statistics as well as subjective(More)
Surprise is informative because it drives attention [1] and modifies learning [2]. Correlates of surprise have been observed at different stages of neural processing, and found to be relevant for learning and memory formation [3]. Although surprise is ubiquitous, there is neither a widely accepted theory that quantitatively links surprise to observed(More)
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of(More)
We propose a new framework for surprise-driven learning that can be used for modeling how humans and animals learn in changing environments. It approximates optimal Bayesian learner, but with significantly reduced computational complexity. This framework consists of two components: (i) a confidence-adjusted surprise measure to capture environmental(More)