Add to ORKGRecurrent neural network processing of regular languages is reasonably well understood. Recent work has examined the less familiar question of context-free languages. Previous results regarding the language a(n)b(n) suggest that while it is possible for a small recurrent network to process context-free languages, learning them is difficult. This paper considers the reasons underlying this difficulty by considering the relationship between the dynamics of the network and weightspace. We are able to show that the dynamics required for the solution lie in a region of weightspace close to a bifurcation point where small changes in weights may result in radically different network behaviour. Furthermore, we show that the error gradient informati...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
Although considerable interest has been shown in language inference and automata induction using rec...
Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languag...
Recurrent neural networks are capable of learning context-free tasks, however learning performance i...
In recent years it has been shown that first order recurrent neural networks trained by gradient-des...
We address the problem of processing a context-sensitive language with a recurrent neural network (R...
This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a Connect...
Recent work by Siegelmann has shown that the computational power of recurrent neural networks matche...
The paper first summarizes a general approach to the training of recurrent neural networks by gradie...
Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recogni...
The long short-term memory (LSTM) is not the only neural network which learns a context sensitive la...
Simple second-order recurrent networks are shown to readily learn small known regular grammars when...
In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
Although considerable interest has been shown in language inference and automata induction using rec...
Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languag...
Recurrent neural networks are capable of learning context-free tasks, however learning performance i...
In recent years it has been shown that first order recurrent neural networks trained by gradient-des...
We address the problem of processing a context-sensitive language with a recurrent neural network (R...
This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a Connect...
Recent work by Siegelmann has shown that the computational power of recurrent neural networks matche...
The paper first summarizes a general approach to the training of recurrent neural networks by gradie...
Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recogni...
The long short-term memory (LSTM) is not the only neural network which learns a context sensitive la...
Simple second-order recurrent networks are shown to readily learn small known regular grammars when...
In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable ...
Although considerable interest has been shown in language inference and automata induction using rec...