Publications by Amir Hesam Salavati

Learning Neural Connectivity from Firing Activity: Scalable Algorithms with Provable Guarantees

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’ ...

Springer New York LLC2018

Computational Characterization of the Dependence of Halide Perovskite Effective Masses on Chemical Composition and Structure

Michael Graetzel, Ursula Röthlisberger, Simone Meloni, Amir Hesam Salavati, Giulia Palermo, Negar Ashari Astani

Effective masses are calculated for a large variety of perovskites of the form ABX(3) differing in chemical composition (A= Na, Li, Cs; B = Pb, Sn; X= Cl, Br, I) and crystal structure. In addition, the effects of some defects and dopants are assessed. We s ...

2017

Learning Network Structures from Firing Patterns

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

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 ...

Ieee2016

Asynchronous Decoding of LDPC Codes over BEC

Amin Karbasi, Amir Hesam Salavati, Saeid Haghighatshoar

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 ...

2015

Different Neuronal Recording Schemes

Amir Hesam Salavati

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 ...

2014

Coding Theory and Neural Associative Memories with Exponential Pattern Retrieval Capacity

Amir Hesam Salavati

EPFL2014

Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning

Mohammad Amin Shokrollahi, Amir Hesam Salavati, Krishna Kumar

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 ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Coupled neural associative memories

Mohammad Amin Shokrollahi, Amin Karbasi, Amir Hesam Salavati

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 ...

IEEE2013

Noise-enhanced associative memories

Mohammad Amin Shokrollahi, Amin Karbasi, Amir Hesam Salavati

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 ...

2013

Multi-Level Error-Resilient Neural Networks

Amin Karbasi, Amir Hesam Salavati

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 ...

IEEE2012