Above are papers related to the following paper: [Paper_ID] Paper #16470. 	[Title] Finite State Machines and Recurrent Neural Networks Automata and Dynamical Systems Approaches [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #16451. 	[Title] Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks [Abstract] A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equivalent to a single RNN learning multiple different synchronous sequential machines. We define such a sequential machine structure as augmented and show that a SRIN is essentially an Augmented Synchronous Sequential Machine (ASSM). As an example, we learn a small six-switch SRIN. After training we extract the net-work's internal representation of the ASSM and corresponding SRIN. fl This paper is adapted from ( Goudreau, 1993, Chapter 6 ) . A shortened version of this paper was published in ( Goudreau & Giles, 1993 ) . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #57764. 	[Title] Inductive Learning by Selection of Minimal Complexity Representations [Abstract]  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 #12337. 	[Title] A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction [Abstract] Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained by gradient descent and an adaptive clustering technique that quantizes the state space. dolce is based on the assumption that a finite set of discrete internal states is required for the task, and that the actual network state belongs to this set but has been corrupted by noise due to inaccuracy in the weights. dolce learns to recover the discrete state with maximum a posteriori probability from the noisy state. Simulations show that dolce leads to a significant improvement in generalization performance over earlier neural net approaches to FSM induction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #253971. 	[Title] Fools Gold: Extracting Finite State Machines From Recurrent Network Dynamics [Abstract] Several recurrent networks have been proposed as representations for the task of formal language learning. After training a recurrent network, the next step is to understand the information processing carried out by the network. Some researchers (Giles et al., 1992; Watrous & Kuhn, 1992; Cleeremans et al., 1989) have resorted to extracting finite state machines from the internal state trajectories of their recurrent networks. This paper describes two conditions, sensitivity to initial conditions and frivolous computational explanations due to discrete measurements (Kolen & Pollack, 1993), which allow these extraction methods to return illusionary finite state descriptions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129683. 	[Title] A Stochastic Search Approach to Grammar Induction [Abstract] This paper describes a new sampling-based heuristic for tree search named SAGE and presents an analysis of its performance on the problem of grammar induction. This last work has been inspired by the Abbadingo DFA learning competition [14] which took place between Mars and November 1997. SAGE ended up as one of the two winners in that competition. The second winning algorithm, first proposed by Rod-ney Price, implements a new evidence-driven heuristic for state merging. Our own version of this heuristic is also described in this paper and compared to SAGE. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129621. 	[Title] Analysis of Dynamical Recognizers [Abstract] Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and IFS-like fractal state sets. Follow-on work focused mainly on the extraction and minimization of a finite state automaton (FSA) from the trained network. However, such networks are capable of inducing languages which are not regular, and therefore not equivalent to any FSA. Indeed, it may be simpler for a small network to fit its training data by inducing such a non-regular language. But when is the network's language not regular? In this paper, using a low dimensional network capable of learning all the Tomita data sets, we present an empirical method for testing whether the language induced by the network is regular or not. We also provide a detailed "-machine analysis of trained networks for both regular and non-regular languages. Please summarise all the given information.

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 #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 #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.

Above are papers related to the following paper: [Paper_ID] Paper #16476. 	[Title] Extraction of Rules from Discrete-Time Recurrent Neural Networks [Abstract] The extraction of symbolic knowledge from trained neural networks and the direct encoding of (partial) knowledge into networks prior to training are important issues. They allow the exchange of information between symbolic and connectionist knowledge representations. The focus of this paper is on the quality of the rules that are extracted from recurrent neural networks. Discrete-time recurrent neural networks can be trained to correctly classify strings of a regular language. Rules defining the learned grammar can be extracted from networks in the form of deterministic finite-state automata (DFA's) by applying clustering algorithms in the output space of recurrent state neurons. Our algorithm can extract different finite-state automata that are consistent with a training set from the same network. We compare the generalization performances of these different models and the trained network and we introduce a heuristic that permits us to choose among the consistent DFA's the model which best approximates the learned regular grammar. Keywords: Recurrent Neural Networks, Grammatical Inference, Regular Languages, Deterministic Finite-State Automata, Rule Extraction, Generalization Performance, Model Selection, Occam's Razor. fl Technical Report CS-TR-3465 and UMIACS-TR-95-54, University of Maryland, College Park, MD 20742. Ac cepted for publication in Neural Networks. Please summarise all the given information.

Paper #16470 has the title `Finite State Machines and Recurrent Neural Networks Automata and Dynamical Systems Approaches  
` and the abstract `nan`.
Question: Which category should Paper #16470 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #16470 has the title `Finite State Machines and Recurrent Neural Networks Automata and Dynamical Systems Approaches  
` and the abstract `nan`.
Question: Which category should Paper #16470 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