Above are papers related to the following paper: [Paper_ID] Paper #688849. 	[Title] In  Stable Dynamic Parameter Adaptation [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133010. 	[Title] Decimatable Boltzmann Machines vs. Gibbs Sampling [Abstract] Exact Boltzmann learning can be done in certain restricted networks by the technique of decimation. We have enlarged the set of dec-imatable Boltzmann machines by introducing a new and more general decimation rule. We have compared solutions of a probability density estimation problem with decimatable Boltzmann machines to the results obtained by Gibbs sampling in unrestricted (non-decimatable) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #300806. 	[Title] Learning in Boltzmann Trees [Abstract] We introduce a large family of Boltzmann machines that can be trained using standard gradient descent. The networks can have one or more layers of hidden units, with tree-like connectivity. We show how to implement the supervised learning algorithm for these Boltzmann machines exactly, without resort to simulated or mean-field annealing. The stochastic averages that yield the gradients in weight space are computed by the technique of decimation. We present results on the problems of N -bit parity and the detection of hidden symmetries. Please summarise all the given information.

Paper #688849 has the title `In  Stable Dynamic Parameter Adaptation  
` and the abstract ` 
`.
Question: Which category should Paper #688849 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #688849 has the title `In  Stable Dynamic Parameter Adaptation  
` and the abstract ` 
`.
Question: Which category should Paper #688849 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