15153136
Using recurrent neural networks to learn the structure of interconnection networks
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 network's internal representation of the ASSM and corresponding SRIN.
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.
3531625
Autonomous citation matching
Advances in computational resources and the communications infrastructure, and the rapid rise of the World Wide Web, have led to the increasingly widespread availability of scientific papers in electronic form. Scientific papers usually contain citations to previous work, and indices of these citations are valuable for literature search , analysis, and evaluation. Current citation indices of the scientific literature are constructed using manual effort and are typically expensive. Part of the reason for using manual effort is the great variability of citation syntax – i t can be difficult to autonomously determine if two citations refer to the same article because citations can be written in many different formats. We present machine learning techniques that identify variant forms of citations to the same paper. A number of algorithms are presented. An algorithm based on word and phrase matching is found to perform best, and is sufficiently accurate for unassisted use in an autonomous citation indexing system. An algorithm based on a string edit distance performs poorly in comparison. A computationally efficient subfield algorithm is also presented. The accuracy and efficiency of all algorithms is quantitatively compared on a number of datasets.
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.
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.
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.
9195365
Accessibility of information on the Web
The publicly indexable WorldWide Web now contains about 800 million pages, encompassing about 6 terabytes of text data on about 3 million servers. The web is increasingly being used in all aspects of society ; for example, consumers use search engines to locate and buy goods, or to research many decisions (such as choosing a holiday destination, medical treatment or election vote). Scientists are increasingly using search engines to locate research of interest: some rarely use libraries, locating research articles primarily online; scientific editors use search engines to locate potential reviewers. Web users spend a lot of their time using search engines to locate material on the vast and unorganized web. About 85% of users use search engines to locate information 1 , and several search engines consistently rank among the top ten sites accessed on the web 2. The Internet and the web are transforming society, and the search engines are an important part of this process. Delayed indexing of scientific research might lead to the duplication of work, and the presence and ranking of online stores in search-engine listings can substantially affect economic viability (some websites are reportedly for sale primarily based on the fact that they are indexed by Yahoo). We previously estimated 3 that the publicly indexable web contained at least 320 million pages in December 1997 (the publicly indexable web excludes pages that are not normally considered for indexing by web search engines, such as pages with authorization requirements, pages excluded from indexing using the robots exclusion standard , and pages hidden behind search forms). We also reported that six major public search engines (AltaVista, Excite, HotBot, Infoseek, Lycos and Northern Light) collectively covered about 60% of the web. The largest coverage of a single engine was about one-third of the estimated total size of the web. We have now obtained and analysed a random sample of servers to investigate the amount and distribution of information on the web. During 2–28 February 1999, we chose random Internet Protocol (IP) addresses, and tested for a web server at the standard port. There are currently 256 4 (about 4.3 billion) possible IP addresses (IPv6, the next version of the IP protocol which is under development, will increase this substantially); some of these are unavailable while some are known to be unassigned. We have tested random IP addresses (with replacement), and have estimated the total number of web …