6860034
Training Second-Order Recurrent Neural Networks using Hints
We investigate a method for inserting rules into discrete-time second-order recurrent neural networks which are trained to recognize regular languages. The rules defining regular languages can be expressed in the form of transitions in the corresponding deterministic finite-state automaton. Inserting these rules as hints into networks with second-order connections is straightforward. Our simulation results show that even weak hints seem to improve the convergence time by an order of magnitude.
30079691
Evaluating Answer Quality/Efficiency Tradeoffs
974804
Predicting Multiprocessor Memory Access Patterns with Learning Models
Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for memory access pattern prediction. In particular three different on-line machine learning prediction techniques were tested to learn and predict repetitive memory access patterns for three typical parallel processing applications, the 2-D relaxation algorithm, matrix multiply and Fast Fourier Transform on a shared memory multiprocessor. The predictions were then used by a routing control algorithm to reduce control latency in the interconnection network by configuring the interconnection network to provide needed memory access paths before they were requested. Three trainable prediction techniques were used and tested: 1). a Markov predictor, 2). a linear predictor and 3). a time delay neural network (TDNN) predictor. Different predictors performed best on different applications, but the TDNN produced uniformly good results.
17331455
Rule Revision With Recurrent Neural Networks
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 (i.e. the rules were not the ones in the randomly generated grammar).
14572455
Effects of Noise on Convergence and Generalization in Recurrent Networks
We introduce and study methods of inserting synaptic noise into dynamically-driven recurrent neural networks and show that applying a controlled amount of noise during training may improve convergence and generalization. In addition, we analyze the effects of each noise parameter (additive vs. multiplicative, cumulative vs. non-cumulative, per time step vs. per string) and predict that best overall performance can be achieved by injecting additive noise at each time step. Extensive simulations on learning the dual parity grammar from temporal strings substantiate these predictions.
14903074
Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants
We propose an algorithm for encoding deterministic finite-state automata (DFAs) in second-order recurrent neural networks with sigmoidal discriminant function and we prove that the languages accepted by the constructed network and the DFA are identical. The desired finite-state network dynamics is achieved by programming a small subset of all weights. A worst case analysis reveals a relationship between the weight strength and the maximum allowed network size, which guarantees finite-state behavior of the constructed network. We illustrate the method by encoding random DFAs with 10, 100, and 1000 states. While the theory predicts that the weight strength scales with the DFA size, we find empirically the weight strength to be almost constant for all the random DFAs. These results can be explained by noting that the generated DFAs represent average cases. We empirically demonstrate the existence of extreme DFAs for which the weight strength scales with DFA size.
39384561
Next generation CiteSeer
CiteSeer, a computer and information science search engine and digital library, has been a radical departure for scientific document access and analysis. With nearly 700,000 documents, it has sometimes two million page views a day making it one of the most popular document access engines in science. CiteSeer is also portable, having been extended to ebusiness (eBizSearch) and more recently to academic business documents (SMEALSearch). CiteSeer is based on two features: actively acquiring new documents and automatic tagging and linking of metadata information inherent in an academic document's syntactic structure. Why is CiteSeer so popular? We discuss this and methods for providing new tagged metadata such as institutions and acknowledgements, new data resources and services and the issues in automation. We then discuss the next generation of CiteSeer.