From January 20 to 25 2008, the Dagstuhl Seminar 08041 ``Recurrent Neural Networks- Models, Capacities, and Applications\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged fro...
Recurrent neural networks (RNNs) offer flexible machine learning tools which share the learning abil...
Neuroadaptive technology (NAT) utilizes real-time measures of neurophysiological activity within a c...
From January 20 to 25 2008, the Dagstuhl Seminar 08041 ``Recurrent Neural Networks- Models, Capaciti...
The seminar centered around recurrent information processing in neural systems and its connections t...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
From 25.07. to 30.07.2010, the Dagstuhl Seminar 10302 ``Learning paradigms in dynamic environments \...
This chapter provides an introduction and motivates the leading thread of the following ten chapters...
The thesis is written in chapter form. Chapter 1 describes some of the history of neural networks...
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neu...
This past year, RNNs have seen a lot of attention as powerful models that are able to decode sequenc...
This collection of articles responds to the urgent need for timely and comprehensive reviews in a mu...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network...
This book is a collection of selected papers from the 21st WIRN workshop, held in Vietri sul Mare, I...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged fro...
Recurrent neural networks (RNNs) offer flexible machine learning tools which share the learning abil...
Neuroadaptive technology (NAT) utilizes real-time measures of neurophysiological activity within a c...
From January 20 to 25 2008, the Dagstuhl Seminar 08041 ``Recurrent Neural Networks- Models, Capaciti...
The seminar centered around recurrent information processing in neural systems and its connections t...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
From 25.07. to 30.07.2010, the Dagstuhl Seminar 10302 ``Learning paradigms in dynamic environments \...
This chapter provides an introduction and motivates the leading thread of the following ten chapters...
The thesis is written in chapter form. Chapter 1 describes some of the history of neural networks...
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neu...
This past year, RNNs have seen a lot of attention as powerful models that are able to decode sequenc...
This collection of articles responds to the urgent need for timely and comprehensive reviews in a mu...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network...
This book is a collection of selected papers from the 21st WIRN workshop, held in Vietri sul Mare, I...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged fro...
Recurrent neural networks (RNNs) offer flexible machine learning tools which share the learning abil...
Neuroadaptive technology (NAT) utilizes real-time measures of neurophysiological activity within a c...