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Towards Predicting the Runtime of Iterative Analytics with PREDIcT

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PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Anastasia Ailamaki, Adrian Daniel Popescu

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

2013

Use of Transient Measurements for Real-Time Optimization via Modifier Adaptation

Dominique Bonvin, Grégory François

Real-time optimization (RTO) methods use measurements to offset the effect of uncertainty and drive the plant to optimality. Explicit RTO schemes, which are characterized by solving a static optimization problem repeatedly, typically require multiple itera ...

2013

Graph-Based Information Processing

Amin Karbasi

We live in a world characterized by massive information transfer and real-time communication. The demand for efficient yet low-complexity algorithms is widespread across different fields, including machine learning, signal processing and communications. Mo ...

EPFL2012

On the effects of topology and node distribution on learning over complex adaptive networks

Ali H. Sayed

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network through their collaborations, as dictated by the network topology and ...

IEEE2011

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...

EPFL2011

Estimating small-world topology of neural networks from multi-electrode recordings

Wulfram Gerstner, Joao Emanuel Felipe Gerhard

Since the seminal work of Watts in the late 90s, graph-theoretic analyses have been performed on many complex dynamic networks, including brain structures. Most studies have focused on functional connectivity defined between whole brain regions, using imag ...

2011

A Greedy Approach To The Distributed Karhunen-Loeve Transform

Michael Christoph Gastpar

In the distributed linear source coding problem a set of distributed sensors observe subsets of a data vector, and provide the fusion center with linearly encoded data. The goal is to determine the encoding matrix of each sensor such that the fusion center ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Vector Network Coding

Christina Fragouli, Javad Ebrahimi Boroojeni

We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying th ...

2010

Recursive Implementation of the Distributed Karhunen-Loeve Transform

Michael Christoph Gastpar

In the distributed linear source coding problem, a set of distributed sensors observe subsets of a data vector with noise, and provide the fusion center linearly encoded data. The goal is to determine the encoding matrix of each sensor such that the fusion ...

2010

Vector Network Coding Algorithms

Christina Fragouli, Javad Ebrahimi Boroojeni

2010