144 citations · 341 across the 27 of their papers we have counts for
6 papers · 1 filter
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution
Amirhossein Javaheri, Jiaxi Ying, Daniel P. Palomar +1
Graph models provide efficient tools to capture the underlying structure of data defined over networks. Many real-world network topologies are subject to change over time. Learning…
Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Amirhossein Javaheri, Arash Amini, Farokh Marvasti +1
Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics,…
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning
Naseh Majidi, Mahdi Shamsi, Farokh Marvasti
Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and pred…
Efficient Sparse Artificial Neural Networks
Seyed Majid Naji, Azra Abtahi, Farokh Marvasti
The brain, as the source of inspiration for Artificial Neural Networks (ANN), is based on a sparse structure. This sparse structure helps the brain to consume less energy, learn ea…
A Convex Similarity Index for Sparse Recovery of Missing Image Samples
Amirhossein Javaheri, Hadi Zayyani, Farokh Marvasti
This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of -norm or Mean S…
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
Ashkan Esmaeili, Arash Amini, Farokh Marvasti
In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix…