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- Sameer Pawar, Nima Noorshams, Salim Y. El Rouayheb, Kannan Ramchandran
- 2011 IEEE International Symposium on Information…
- 2011

We introduce an efficient family of exact regenerating codes for data storage in large-scale distributed systems. We refer to these new codes as Distributed Replication-based Exact Simple Storage (DRESS) codes. A key property of DRESS codes is their very efficient distributed and uncoded repair and growth processes that have minimum bandwidth, reads and… (More)

- Nima Noorshams, Martin J. Wainwright
- Journal of Machine Learning Research
- 2013

The sum-product or belief propagation (BP) algorithm is a widely used message-passing technique for computing approximate marginals in graphical models. We introduce a new technique, called stochastic orthogonal series message-passing (SOSMP), for computing the BP fixed point in models with continuous random variables. It is based on a deterministic… (More)

- Nima Noorshams, Martin J. Wainwright
- IEEE Transactions on Information Theory
- 2013

The belief propagation (BP) or sum-product algorithm is a widely used message-passing method for computing marginal distributions in graphical models. At the core of the BP message updates, when applied to a graphical model involving discrete variables with pairwise interactions, lies a matrix-vector product with complexity that is quadratic in the state… (More)

- Nima Noorshams, Martin J. Wainwright
- 2011 49th Annual Allerton Conference on…
- 2011

The sum-product or belief propagation (BP) algorithm is widely used to compute exact or approximate marginals in graphical models. However, for graphical models with continuous or high-dimensional discrete states and/or high degree factors, it can be computationally expensive to update messages. We propose the stochastic belief propagation algorithm (SBP)… (More)

- Nima Noorshams, Mehdi Malboubi, Ahmad Bahai
- 2010 IEEE 11th International Workshop on Signal…
- 2010

In this paper, the cooperative spectrum sensing is probabilistically modeled as a mixture of two Gaussian distributions and EM algorithm is applied for learning the parameters and classifying these two classes. Also, in order to exploit the dependencies of the states of the primary user in time, a Hidden Markov Model is used to improve the performance of… (More)

- Nima Noorshams, Martin J. Wainwright
- IEEE Journal of Selected Topics in Signal…
- 2011

The problem of network-constrained averaging is to compute the average of a set of values distributed throughout a graph <i>G</i> using an algorithm that can pass messages only along graph edges. We study this problem in the noisy setting, in which the communication along each link is modeled by an additive white Gaussian noise channel. We propose a… (More)

- Nima Noorshams, Martin J. Wainwright
- 2012 IEEE International Symposium on Information…
- 2012

Belief propagation (BP) is a widely used algorithm for computing the marginal distributions in graphical models. However, in applications involving continuous random variables, the messages themselves are real-valued functions, which leads to significant computational bottlenecks. In this paper, we propose a low complexity method for performing belief… (More)

We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t), t = 1,. .. , T , for the problem of underdetermined Sparse Component Analysis (SCA). Contrary to most previous algorithms, there can be more than one dominant source at each instant (we call it a " multiple dominant " problem). The main idea is to convert the… (More)

- Nima Noorshams, Martin J. Wainwright
- 2010 IEEE International Symposium on Information…
- 2010

The problem of network-constrained averaging is to compute the average of a collection of a set of values distributed throughout a network using an algorithm that can pass messages only along edges of the network. We study this problem in the noisy setting, in which the communication along each link is modeled by an additive white Gaussian noise channel. We… (More)

- Nima Noorshams, Aravind Iyengar
- ArXiv
- 2014

Low-density parity-check codes, a class of capacity-approaching linear codes, are particularly recognized for their efficient decoding scheme. The decoding scheme, known as the sum-product, is an iterative algorithm consisting of passing messages between variable and check nodes of the factor graph. The sum-product algorithm is fully parallelizable, owing… (More)