Above are papers related to the following paper: [Paper_ID] Paper #907845. 	[Title] Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality [Abstract] This paper investigates a technique for creating sparsely connected feed-forward neural networks which may be capable of producing networks that have very large input and output layers. The architecture appears to be particularly suited to tasks that involve sparse training data as it is able to take advantage of the sparseness to further reduce training time. Some initial results are presented based on tests on the 16 bit compression problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #145134. 	[Title] Brief Papers Computing Second Derivatives in Feed-Forward Networks: A Review [Abstract] The calculation of second derivatives is required by recent training and analysis techniques of connectionist networks, such as the elimination of superfluous weights, and the estimation of confidence intervals both for weights and network outputs. We here review and develop exact and approximate algorithms for calculating second derivatives. For networks with jwj weights, simply writing the full matrix of second derivatives requires O(jwj 2 ) operations. For networks of radial basis units or sigmoid units, exact calculation of the necessary intermediate terms requires of the order of 2h + 2 backward/forward-propagation passes where h is the number of hidden units in the network. We also review and compare three approximations (ignoring some components of the second derivative, numerical differentiation, and scoring). Our algorithms apply to arbitrary activation functions, networks, and error functions (for instance, with connections that skip layers, or radial basis functions, or cross-entropy error and Softmax units, etc.). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129287. 	[Title] The Free Speech  Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks [Abstract] This paper presents new methods for training large neural networks for phoneme probability estimation. An architecture combining timedelay windows and recurrent connections is used to capture the important dynamic information of the speech signal. Because the number of connections in a fully connected recurrent network grows super-linear with the number of hidden units, schemes for sparse connection and connection pruning are explored. It is found that sparsely connected networks outperform their fully connected counterparts with an equal number of connections. The implementation of the combined architecture and training scheme is described in detail. The networks are evaluated in a hybrid HMM/ANN system for phoneme recognition on the TIMIT database, and for word recognition on the WAXHOLM database. The achieved phone error-rate, 27.8%, for the standard 39 phoneme set on the core testset of the TIMIT database is in the range of the lowest reported. All training and simulation software used is made freely available by the author, and detailed information about the software and the training process is given in an Appendix. Please summarise all the given information.

Paper #907845 has the title `Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality  
` and the abstract `This paper investigates a technique for creating sparsely connected feed-forward neural networks which may be capable of producing networks that have very large input and output layers. The architecture appears to be particularly suited to tasks that involve sparse training data as it is able to take advantage of the sparseness to further reduce training time. Some initial results are presented based on tests on the 16 bit compression problem. 
`.
Question: Which category should Paper #907845 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #907845 has the title `Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality  
` and the abstract `This paper investigates a technique for creating sparsely connected feed-forward neural networks which may be capable of producing networks that have very large input and output layers. The architecture appears to be particularly suited to tasks that involve sparse training data as it is able to take advantage of the sparseness to further reduce training time. Some initial results are presented based on tests on the 16 bit compression problem. 
`.
Question: Which category should Paper #907845 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