Above are papers related to the following paper: [Paper_ID] Paper #1131195. 	[Title] Simple Synchrony Networks: Learning Generalisations across Syntactic Constituents [Abstract] This paper describes a training algorithm for Simple Synchrony Networks (SSNs), and reports on experiments in language learning using a recursive grammar. The SSN is a new connectionist architecture combining a technique for learning about patterns across time, Simple Recurrent Networks (SRNs), with Temporal Synchrony Variable Binding (TSVB). The use of TSVB means the SSN can learn about entities in the training set, and generalise this information to entities in the test set. In the experiments, the network is trained on sentences with up to one embedded clause, and with some words restricted to certain classes of constituent. During testing, the network generalises information learned to sentences with up to three embedded clauses, and with words appearing in any constituent. These results demonstrate that SSNs learn generalisations across syntactic constituents. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #43165. 	[Title] Natural Language Grammatical Inference with Recurrent Neural Networks [Abstract] This paper examines the inductive inference of a complex grammar with neural networks specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government-and-Binding theory. Neural networks are trained, without the division into learned vs. innate components assumed by Chomsky, in an attempt to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. How a recurrent neural network could possess linguistic capability, and the properties of various common recurrent neural network architectures are discussed. The problem exhibits training behavior which is often not present with smaller grammars, and training was initially difficult. However, after implementing several techniques aimed at improving the convergence of the gradient descent backpropagation-through- time training algorithm, significant learning was possible. It was found that certain architectures are better able to learn an appropriate grammar. The operation of the networks and their training is analyzed. Finally, the extraction of rules in the form of deterministic finite state automata is investigated. Please summarise all the given information.

Paper #1131195 has the title `Simple Synchrony Networks: Learning Generalisations across Syntactic Constituents  
` and the abstract `This paper describes a training algorithm for Simple Synchrony Networks (SSNs), and reports on experiments in language learning using a recursive grammar. The SSN is a new connectionist architecture combining a technique for learning about patterns across time, Simple Recurrent Networks (SRNs), with Temporal Synchrony Variable Binding (TSVB). The use of TSVB means the SSN can learn about entities in the training set, and generalise this information to entities in the test set. In the experiments, the network is trained on sentences with up to one embedded clause, and with some words restricted to certain classes of constituent. During testing, the network generalises information learned to sentences with up to three embedded clauses, and with words appearing in any constituent. These results demonstrate that SSNs learn generalisations across syntactic constituents. 
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Question: Which category should Paper #1131195 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131195 has the title `Simple Synchrony Networks: Learning Generalisations across Syntactic Constituents  
` and the abstract `This paper describes a training algorithm for Simple Synchrony Networks (SSNs), and reports on experiments in language learning using a recursive grammar. The SSN is a new connectionist architecture combining a technique for learning about patterns across time, Simple Recurrent Networks (SRNs), with Temporal Synchrony Variable Binding (TSVB). The use of TSVB means the SSN can learn about entities in the training set, and generalise this information to entities in the test set. In the experiments, the network is trained on sentences with up to one embedded clause, and with some words restricted to certain classes of constituent. During testing, the network generalises information learned to sentences with up to three embedded clauses, and with words appearing in any constituent. These results demonstrate that SSNs learn generalisations across syntactic constituents. 
`.
Question: Which category should Paper #1131195 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