Publication

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Publications associées (36)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

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

Multimode optical fiber transmission with a deep learning network

Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Babak Rahmani, Georgia Konstantinou

Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent light propagating within them to produce seemingly random patterns. Thus, for applications such as imaging and image projection through an MMF, careful measur ...

CHINESE ACAD SCIENCES, CHANGCHUN INST OPTICS FINE MECHANICS AND PHYSICS2018

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

When Neurons Fail - Technical Report

Rachid Guerraoui, El Mahdi El Mhamdi

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...

EPFL2016

Network Alignment: Theory, Algorithms, and Applications

Ehsan Kazemi

Networks are central in the modeling and analysis of many large-scale human and technical systems, and they have applications in diverse fields such as computer science, biology, social sciences, and economics. Recently, network mining has been an active a ...

EPFL2016

Simplifying Development and Management of Software-Defined Networks

Peter Peresini

Computer networks are an important part of our life. Yet, they are traditionally hard to manage and operate. The recent shift to Software-Defined Networking (SDN) is promised to change the way in which the networks are run by their operators. However, SDN ...

EPFL2016

Distributed machine learning in networks by consensus

Martin Hasler, Leonidas Georgopoulos

We propose an algorithm to learn from distributed data on a network of arbitrarily connected machines without exchange of the data-points. Parts of the dataset are processed locally at each machine, and then the consensus communication algorithm is employe ...

Elsevier2014

On the Influence of Informed Agents on Learning and Adaptation Over Networks

Ali H. Sayed

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...

Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States2013

Computing the size and number of neuronal clusters in local circuits

Henry Markram, Rodrigo de Campos Perin, Martin Telefont

The organization of connectivity in neuronal networks is fundamental to understanding the activity and function of neural networks and information processing in the brain. Recent studies show that the neocortex is not only organized in columns and layers b ...

Frontiers Research Foundation2013

Is the Network Turing-Complete?

Dejan Kostic, Peter Peresini

Ensuring correct network behavior is hard. This is the case even for simple networks, and adding middleboxes only complicates this task. In this paper, we demonstrate a fundamental property of networks. Namely, we show a way of using a network to emulate t ...

2013