Above are papers related to the following paper: [Paper_ID] Paper #51831. 	[Title] Using Prior Knowledge in an NNPDA to Learn Context-Free Languages [Abstract] Although considerable interest has been shown in language inference and automata induction using recurrent neural networks, success of these models has mostly been limited to regular languages. We have previously demonstrated that Neural Network Pushdown Automaton (NNPDA) model is capable of learning deterministic context-free languages (e.g., a n b n and parenthesis languages) from examples. However, the learning task is computationally intensive. In this paper we discuss some ways in which a priori knowledge about the task and data could be used for efficient learning. We also observe that such knowledge is often an experimental prerequisite for learning nontrivial languages (eg. a n b n cb m a m ). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #395725. 	[Title] Distributed Representations and Nested Compositional Structure [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.

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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 #16474. 	[Title] Rule Revision with Recurrent Neural Networks [Abstract] Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge (or rules) into recurrent neural networks, we show that recurrent neural networks are able to perform rule revision. Rule revision is performed by comparing the inserted rules with the rules in the finite-state automata extracted from trained networks. The results from training a recurrent neural network to recognize a known non-trivial, randomly generated regular grammar show that not only do the networks preserve correct rules but that they are able to correct through training inserted rules which were initially incorrect. (By incorrect, we mean that the rules were not the ones in the randomly generated grammar.) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #152219. 	[Title] A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages [Abstract] We present a neural net architecture that can discover hierarchical and recursive structure in symbol strings. To detect structure at multiple levels, the architecture has the capability of reducing symbols substrings to single symbols, and makes use of an external stack memory. In terms of formal languages, the architecture can learn to parse strings in an LR(0) context-free grammar. Given training sets of positive and negative exemplars, the architecture has been trained to recognize many different grammars. The architecture has only one layer of modifiable weights, allowing for a Many cognitive domains involve complex sequences that contain hierarchical or recursive structure, e.g., music, natural language parsing, event perception. To illustrate, "the spider that ate the hairy fly" is a noun phrase containing the embedded noun phrase "the hairy fly." Understanding such multilevel structures requires forming reduced descriptions (Hinton, 1988) in which a string of symbols or states ("the hairy fly") is reduced to a single symbolic entity (a noun phrase). We present a neural net architecture that learns to encode the structure of symbol strings via such reduction transformations. The difficult problem of extracting multilevel structure from complex, extended sequences has been studied by Mozer (1992), Ring (1993), Rohwer (1990), and Schmidhuber (1992), among others. While these previous efforts have made some straightforward interpretation of its behavior. Please summarise all the given information.

Paper #51831 has the title `Using Prior Knowledge in an NNPDA to Learn Context-Free Languages  
` and the abstract `Although considerable interest has been shown in language inference and automata induction using recurrent neural networks, success of these models has mostly been limited to regular languages. We have previously demonstrated that Neural Network Pushdown Automaton (NNPDA) model is capable of learning deterministic context-free languages (e.g., a n b n and parenthesis languages) from examples. However, the learning task is computationally intensive. In this paper we discuss some ways in which a priori knowledge about the task and data could be used for efficient learning. We also observe that such knowledge is often an experimental prerequisite for learning nontrivial languages (eg. a n b n cb m a m ).
`.
Question: Which category should Paper #51831 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #51831 has the title `Using Prior Knowledge in an NNPDA to Learn Context-Free Languages  
` and the abstract `Although considerable interest has been shown in language inference and automata induction using recurrent neural networks, success of these models has mostly been limited to regular languages. We have previously demonstrated that Neural Network Pushdown Automaton (NNPDA) model is capable of learning deterministic context-free languages (e.g., a n b n and parenthesis languages) from examples. However, the learning task is computationally intensive. In this paper we discuss some ways in which a priori knowledge about the task and data could be used for efficient learning. We also observe that such knowledge is often an experimental prerequisite for learning nontrivial languages (eg. a n b n cb m a m ).
`.
Question: Which category should Paper #51831 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