Above are papers related to the following paper: [Paper_ID] Paper #127940. 	[Title] TRAINREC: A System for Training Feedforward Simple Recurrent Networks Efficiently and Correctly [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #416964. 	[Title] Distributed Patterns as Hierarchical Structures [Abstract] Recursive Auto-Associative Memory (RAAM) structures show promise as a general representation vehicle that uses distributed patterns. However training is often difficult, which explains, at least in part, why only relatively small networks have been studied. We show a technique for transforming any collection of hierarchical structures into a set of training patterns for a sequential RAAM which can be effectively trained using a simple (Elman-style) recurrent network. Tr aining produces a set of distributed patterns corresponding to the structures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #243274. 	[Title] Strategies for the Parallel Training of Simple Recurrent Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #16437. 	[Title] Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory [Abstract] This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a Connectionist paradigm using a Recurrent Neural Network Pushdown Automaton (NNPDA). The NNPDA consists of a recurrent neural network connected to an external stack memory through a common error function. We show that the NNPDA is able to learn the dynamics of an underlying pushdown automaton from examples of grammatical and non-grammatical strings. Not only does the network learn the state transitions in the automaton, it also learns the actions required to control the stack. In order to use continuous optimization methods, we develop an analog stack which reverts to a discrete stack by quantization of all activations, after the network has learned the transition rules and stack actions. We further show an enhancement of the network's learning capabilities by providing hints. In addition, an initial comparative study of simulations with first, second and third order recurrent networks has shown that the increased degree of freedom in a higher order networks improve generalization but not necessarily learning speed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #430329. 	[Title] AN ADAPTIVE NEURAL NETWORK PARSER [Abstract] We inv estigate the applicability of an adaptive neural network to problems with time-dependent input by demonstrating that a deterministic parser for natural language inputs of significant syntactic complexity can be developed using recurrent connectionist architectures. The traditional stacking mechanism, known to be necessary for proper treatment of context-free languages in symbolic systems, is absent from the design, having been subsumed by recurrency in the network. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114364. 	[Title] A Neural Network Model for the Gold Market [Abstract] A neural network trend predictor for the gold bullion market is presented. A simple recurrent neural network was trained to recognize turning points in the gold market based on a to-date history of ten market indices. The network was tested on data that was held back from training, and a significant amount of predictive power was observed. The turning point predictions can be used to time transactions in the gold bullion and gold mining company stock index markets to obtain a significant paper profit during the test period. The training data consisted of daily closing prices for the ten input markets for a period of about five years. The turning point targets were labeled for the training phase without the help of a financial expert. Thus, this experiment shows that useful predictions can be made without the use of more extensive market data or knowledge. Please summarise all the given information.

Paper #127940 has the title `TRAINREC: A System for Training Feedforward Simple Recurrent Networks Efficiently and Correctly  
` and the abstract `nan`.
Question: Which category should Paper #127940 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #127940 has the title `TRAINREC: A System for Training Feedforward Simple Recurrent Networks Efficiently and Correctly  
` and the abstract `nan`.
Question: Which category should Paper #127940 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks