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.
14757404
Intelligent Methods for File System Optimization
The speed of I/O components is it major limitation of the speed of all other major components in today's computer systems. Motivated by this, we investigated several algorithms for efficient and intelligent organization of files on a hard disk. Total access time may be decreased if files with temporal locality also have spatial locality. Three intelligent methods based on file type, frequency, and transition probabilities information showed up to 60% savings of total I/O time over the naive placement of files. More computationally intensive hill climbing and genetic algorithms approaches did not outperform statistical methods. The experiments were run on a real and simulated hard drive in single and multiple user environments.
2543653
Extracting and Learning an Unknown Grammar with Recurrent Neural Networks
Simple second-order recurrent networks are shown to readily learn small known regular grammars when trained with positive and negative strings examples. We show that similar methods are appropriate for learning unknown grammars from examples of their strings. The training algorithm is an incremental real-time, recurrent learning (RTRL) method that computes the complete gradient and updates the weights at the end of each string. After or during training, a dynamic clustering algorithm extracts the production rules that the neural network has learned. The methods are illustrated by extracting rules from unknown deterministic regular grammars. For many cases the extracted grammar outperforms the neural net from which it was extracted in correctly classifying unseen strings.
13868805
Architecture of a metasearch engine that supports user information needs
When a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Users with specific needs, such as “research papers” or “homepages,” are not able to express these needs in a way that affects the decisions made by the metasearch engine. In this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. Users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. Our architecture combines several powerful approaches together in a single general purpose metasearch engine.
1872756
Computational capabilities of recurrent NARX neural networks
Recently, fully connected recurrent neural networks have been proven to be computationally rich-at least as powerful as Turing machines. This work focuses on another network which is popular in control applications and has been found to be very effective at learning a variety of problems. These networks are based upon Nonlinear AutoRegressive models with eXogenous Inputs (NARX models), and are therefore called NARX networks. As opposed to other recurrent networks, NARX networks have a limited feedback which comes only from the output neuron rather than from hidden states. They are formalized by y(t)=Psi(u(t-n(u)), ..., u(t-1), u(t), y(t-n(y)), ..., y(t-1)) where u(t) and y(t) represent input and output of the network at time t, n(u) and n(y) are the input and output order, and the function Psi is the mapping performed by a Multilayer Perceptron. We constructively prove that the NARX networks with a finite number of parameters are computationally as strong as fully connected recurrent networks and thus Turing machines. We conclude that in theory one can use the NARX models, rather than conventional recurrent networks without any computational loss even though their feedback is limited. Furthermore, these results raise the issue of what amount of feedback or recurrence is necessary for any network to be Turing equivalent and what restrictions on feedback limit computational power.
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.