9138774
Learning with Product Units
Product units provide a method of automatically learning the higher-order input combinations required for efficient learning in neural networks. However, we show that problems are encountered when using backpropagation to train networks containing these units. This paper examines these problems, and proposes some atypical heuristics to improve learning. Using these heuristics a constructive method is introduced which solves well-researched problems with significantly less neurons than previously reported. Secondly, product units are implemented as candidate units in the Cascade Correlation (Fahlman & Lebiere, 1990) system. This resulted in smaller networks which trained faster than when using sigmoidal or Gaussian units.
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.
30079691
Evaluating Answer Quality/Efficiency Tradeoffs
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.
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).
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.
214314
Clustering and identifying temporal trends in document databases
We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying regularity often found in hyper-linked document databases. Because of this scalability, we can use our method to study the temporal trends of individual clusters in a statistically meaningful manner. As an example of our approach, we give a summary of the temporal trends found in a scientific literature database with thousands of documents.
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.
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 …
6455989
Winners don't take all: Characterizing the competition for links on the web
As a whole, the World Wide Web displays a striking “rich get richer” behavior, with a relatively small number of sites receiving a disproportionately large share of hyperlink references and traffic. However, hidden in this skewed global distribution, we discover a qualitatively different and considerably less biased link distribution among subcategories of pages—for example, among all university homepages or all newspaper homepages. Although the connectivity distribution over the entire web is close to a pure power law, we find that the distribution within specific categories is typically unimodal on a log scale, with the location of the mode, and thus the extent of the rich get richer phenomenon, varying across different categories. Similar distributions occur in many other naturally occurring networks, including research paper citations, movie actor collaborations, and United States power grid connections. A simple generative model, incorporating a mixture of preferential and uniform attachment, quantifies the degree to which the rich nodes grow richer, and how new (and poorly connected) nodes can compete. The model accurately accounts for the true connectivity distributions of category-specific web pages, the web as a whole, and other social networks.
5994026
Extraction and search of chemical formulae in text documents on the web
Often scientists seek to search for articles on the Web related to a particular chemical. When a scientist searches for a chemical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching results in two problems for this domain: a) if the author searches for CH4 and the article has H4C, the article is not returned, and b) ambiguous searches like "He" return all documents where Helium is mentioned as well as documents where the pronoun "he" occurs. To remedy these deficiencies, we propose a chemical formula search engine. To build a chemical formula search engine, we must solve the following problems: 1) extract chemical formulae from text documents, 2) index chemical formulae, and 3) designranking functions for the chemical formulae. Furthermore, query models are introduced for formula search, and for each a scoring scheme based on features of partial formulae is proposed tomeasure the relevance of chemical formulae and queries. We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines(SVM), and a probabilistic model based on conditional random fields (CRF). Different methods for SVM and CRF to tune the trade-off between recall and precision forim balanced data are proposed to improve the overall performance. A feature selection method based on frequency and discrimination isused to remove uninformative and redundant features. Experiments show that our approaches to chemical formula extraction work well, especially after trade-off tuning. The results also demonstrate that feature selection can reduce the index size without changing ranked query results much.