Skip to search formSkip to main contentSkip to account menu

Random neural network

Known as: RNN 
The random neural network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals. It… 
Wikipedia (opens in a new tab)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent… 
Highly Cited
2015
Highly Cited
2015
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have… 
Highly Cited
2014
Highly Cited
2014
In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks… 
Highly Cited
2014
Highly Cited
2014
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more… 
Highly Cited
2014
Highly Cited
2014
Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The… 
Highly Cited
2013
Highly Cited
2013
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist… 
Highly Cited
2010
Highly Cited
2010
A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results… 
Highly Cited
1997
Highly Cited
1997
In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network… 
Highly Cited
1992
Highly Cited
1992
  • E. Gelenbe
  • Neural Computation
  • 1992
  • Corpus ID: 38667978
The capacity to learn from examples is one of the most desirable features of neural network models. We present a learning… 
Highly Cited
1989
Highly Cited
1989
  • E. Gelenbe
  • Neural Computation
  • 1989
  • Corpus ID: 207737442
We introduce a new class of random neural networks in which signals are either negative or positive. A positive signal arriving…