Publications by Amir Hesam Salavati

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We consider the problem of neural association for a network of nonbinary neurons. Here, the task is to first memorize a set of patterns using a network of neurons whose states assume values from a finite number of integer levels. Later, the same network sh ...

LDPC codes are typically decoded by running a synchronous message passing algorithm over the corresponding bipartite factor graph (made of variable and check nodes). More specifically, each synchronous round consists of 1) updating all variable nodes based ...

Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms have allowed reliable learning and recall of an exponential number of patterns. Although these designs correct external errors in recall, they ...

The problem of neural network association is to retrieve a previously memorized pattern from its noisy version using a network of neurons. An ideal neural network should include three components simultaneously: a learning algorithm, a large pattern retriev ...

In this paper, we present a decentralized algorithm to find minimum cost quality of service (QoS) flow subgraphs in network coded multicast schemes. The main objective is to find minimum cost subgraphs that also satisfy user-specified QoS constraints, spec ...

We consider ensembles of binary linear error correcting codes, obtained by sampling each column of the generator matrix G or parity check matrix H independently from the set of all binary vectors of weight d (of appropriate dimension). We investigate the c ...

We propose a novel architecture to design a neural associative memory that is capable of learning a large number of patterns and recalling them later in presence of noise. It is based on dividing the neurons into local clusters and parallel plains, an arch ...

How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes’ behaviors (e.g., the time ...

We consider the problem of neural association, which deals with the retrieval of a previously memorized pattern from its noisy version. The performance of various neural networks developed for this task may be judged in terms of their pattern retrieval cap ...

Having access to neuronal recordings is essential for any network inference algorithm. However, the term ”recording” covers many different schemes which vary significantly in technology, type of output and abundance of data. This report aims to briefly ove ...

Amir Hesam Salavati

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Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning

Asynchronous Decoding of LDPC Codes over BEC

Noise-enhanced associative memories

Multi-Level Error-Resilient Neural Networks

QoSNC: A Novel Approach to QoS-Based Network Coding for Fixed Networks

Phase Transitions for Mutual Information

Coupled neural associative memories

Learning Network Structures from Firing Patterns

Neural Pre-coding Increases the Pattern Retrieval Capacity of Hopﬁeld and Bidirectional Associative Memories

Different Neuronal Recording Schemes

Coding Theory and Neural Associative Memories with Exponential Pattern Retrieval Capacity