Publication

A New Fast Training Algorithm of the WTM Kohonen Neural Network Implemented for Classification of Biomedical Signals

Related publications (84)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Optimized Quantization in Distributed Graph Signal Processing

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates er ...

IEEE2019

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

Traffic Receipts for Network Transparency

Pavlos Nikolopoulos

Today's Internet is not transparent: when packets get lost or delayed, there is typically no information about where the problem occurred, hence no information about who is responsible. This results in Internet service providers (ISPs) offering service lev ...

EPFL2018

The morning commute in urban areas with heterogeneous trip lengths

Nikolaos Geroliminis, Raphael Ali Francis Lamotte

This paper investigates the equilibrium properties of the morning commute problem at the network level with heterogeneous trip lengths. Congestion is modeled with a Macroscopic Fundamental Diagram relating the space-mean speed of a network to the vehicular ...

2018

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Ali H. Sayed

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

IEEE2018

Modeling and Evaluation of Mobility Management in mmWave Cellular Networks

5G cellular networks will enable new differentiated services and applications to support industry like healthcare, energy, automobile and security. These services will massively increase the number of attached devices like mobiles, sensors, and actuators, ...

Ieee2018

Online Resource Inference in Network Utility Maximization Problems

Pascal Frossard, Stefano D'Aronco

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resource ...

2018

Alignment and Assembly

Lyudmila Yartseva

Over recent years, many large network datasets become available, giving rise to novel and valuable applications of data mining and machine learning techniques. These datasets include social networks, the structure of the Internet, and protein-interaction n ...

EPFL2017

Synthesis of single and interplant non-isothermal water networks

François Maréchal, Maziar Kermani

This paper addresses the synthesis problem of non-isothermal water networks using a mathematical programming approach. A heat-integrated water network superstructure and its corresponding mixed integer nonlinear programming (MINLP) model is proposed for th ...

2017

From recommender systems to spatio-temporal dynamics with network science

Kirell Maël Benzi

Networks are data structures that are fundamental for capturing and analyzing complex interactions between objects. While they have been used for decades to solve problems in virtually all scientific fields, their usage for data analysis in real-world prac ...

EPFL2017