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|---|---|---|---|---|
2,094 | 3 | Title: Interpretation of Complex Scenes Using Bayesian Networks
Abstract: In most object recognition systems, interactions between objects in a scene are ignored and the best interpretation is considered to be the set of hypothesized objects that matches the greatest number of image features. We show how image interpretation can be cast as the problem of finding the most probable explanation (MPE) in a Bayesian network that models both visual and physical object interactions. The problem of how to determine exact conditional probabilities for the network is shown to be unimportant, since the goal is to find the most probable configuration of objects, not to calculate absolute probabilities. We furthermore show that evaluating configurations by feature counting is equivalent to calculating the joint probability of the configuration using a restricted Bayesian network, and derive the assumptions about probabilities necessary to make a Bayesian formulation reasonable. | [
2164
] | Validation |
2,095 | 2 | Title: A Practical Monte Carlo Implementation of Bayesian Learning
Abstract: | [
157,
2056,
2230
] | Train |
2,096 | 5 | Title: A Note on Scheduling Algorithms for Processors with Lookahead
Abstract: | [
2142
] | Validation |
2,097 | 3 | Title: Impediments to Universal Preference-Based Default Theories
Abstract: Research on nonmonotonic and default reasoning has identified several important criteria for preferring alternative default inferences. The theories of reasoning based on each of these criteria may uniformly be viewed as theories of rational inference, in which the reasoner selects maximally preferred states of belief. Though researchers have noted some cases of apparent conflict between the preferences supported by different theories, it has been hoped that these special theories of reasoning may be combined into a universal logic of nonmonotonic reasoning. We show that the different categories of preferences conflict more than has been realized, and adapt formal results from social choice theory to prove that every universal theory of default reasoning will violate at least one reasonable principle of rational reasoning. Our results can be interpreted as demonstrating that, within the preferential framework, we cannot expect much improvement on the rigid lexicographic priority mechanisms that have been proposed for conflict resolution. | [
1994,
1995
] | Validation |
2,098 | 6 | Title: Predicting a binary sequence almost as well as the optimal biased coin
Abstract: We apply the exponential weight algorithm, introduced and Littlestone and Warmuth [17] and by Vovk [24] to the problem of predicting a binary sequence almost as well as the best biased coin. We first show that for the case of the logarithmic loss, the derived algorithm is equivalent to the Bayes algorithm with Jeffrey's prior, that was studied by Xie and Barron under probabilistic assumptions [26]. We derive a uniform bound on the regret which holds for any sequence. We also show that if the empirical distribution of the sequence is bounded away from 0 and from 1, then, as the length of the sequence increases to infinity, the difference between this bound and a corresponding bound on the average case regret of the same algorithm (which is asymptotically optimal in that case) is only 1=2. We show that this gap of 1=2 is necessary by calculating the regret of the min-max optimal algorithm for this problem and showing that the asymptotic upper bound is tight. We also study the application of this algorithm to the square loss and show that the algorithm that is derived in this case is different from the Bayes algorithm and is better than it for prediction in the worst-case. | [
453,
2099,
2156
] | Validation |
2,099 | 6 | Title: Game Theory, On-line Prediction and Boosting
Abstract: We study the close connections between game theory, on-line prediction and boosting. After a brief review of game theory, we describe an algorithm for learning to play repeated games based on the on-line prediction methods of Littlestone and War-muth. The analysis of this algorithm yields a simple proof of von Neumann's famous minmax theorem, as well as a provable method of approximately solving a game. We then show that the on-line prediction model is obtained by applying this game-playing algorithm to an appropriate choice of game and that boosting is obtained by applying the same algorithm to the dual of this game. | [
453,
456,
569,
2098
] | Validation |
2,100 | 5 | Title: GURRR: A Global Unified Resource Requirements Representation
Abstract: When compiling for instruction level parallelism (ILP), the integration of the optimization phases can lead to an improvement in the quality of code generated. However, since several different representations of a program are used in the various phases, only a partial integration has been achieved to date. We present a program representation that combines resource requirements and availability information with control and data dependence information. The representation enables the integration of several optimizing phases, including transformations, register allocation, and instruction scheduling. The basis of this integration is the simultaneous allocation of different types of resources. We define the representation and show how it is constructed. We then formulate several optimization phases to use the representation to achieve better integration. | [
1961,
2527
] | Train |
2,101 | 1 | Title: Evolving Control Structures with Automatically Defined Macros Evolving Control Structures with Automatically Defined Macros.
Abstract: When compiling for instruction level parallelism (ILP), the integration of the optimization phases can lead to an improvement in the quality of code generated. However, since several different representations of a program are used in the various phases, only a partial integration has been achieved to date. We present a program representation that combines resource requirements and availability information with control and data dependence information. The representation enables the integration of several optimizing phases, including transformations, register allocation, and instruction scheduling. The basis of this integration is the simultaneous allocation of different types of resources. We define the representation and show how it is constructed. We then formulate several optimization phases to use the representation to achieve better integration. | [
2470
] | Validation |
2,102 | 1 | Title: The Evolution of Communication Schemes Over Continuous Channels
Abstract: Many problems impede the design of multi-agent systems, not the least of which is the passing of information between agents. While others hand implement communication routes and semantics, we explore a method by which communication can evolve. In the experiments described here, we model agents as connectionist networks. We supply each agent with a number of communications channels implemented by the addition of both input and output units for each channel. The output units initiate environmental signals whose amplitude decay over distance and are perturbed by environmental noise. An agent does not receive input from other individuals, rather the agents input reects the summation of all other agents output signals along that channel. Because we use real-valued activations, the agents communicate using real-valued vectors. Under our evolutionary program, GNARL, the agents coevolve a communication scheme over continuous channels which conveys task-spe cific information. | [
144,
189,
2664
] | Train |
2,103 | 1 | Title: The Evolution of Communication Schemes Over Continuous Channels
Abstract: As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system presented in [Koza and Andre 1995] is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a SIMD architecture, except for a data-parallel approach [Tufts 1995], although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor. The aim of this chapter is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer cost-effective cycles for scientific experimentation, this is a useful approach. The idea of simulating a MIMD machine using a SIMD architecture is not new [Hillis and Steele 1986; Littman and Metcalf 1990; Dietz and Cohen 1992]. One of the original ideas for the Connection Machine [Hillis and Steele 1986] was that it could simulate other parallel architectures. Indeed, in the extreme, each processor on a SIMD architecture can simulate a universal Turing machine (TM). With different turing machine specifications stored in each local memory, each processor would simply have its own tape, tape head, state table and state pointer, and the simulation would be performed by repeating the basic TM operations simultaneously. Of course, such a simulation would be very inefficient, and difficult to program, but would have the advantage of being really MIMD, where no SIMD processor would be in idle state, until its simulated machine halts. Now let us consider an alternative idea, that each SIMD processor would simulate an individual stored program computer using a simple instruction set. For each step of the simulation, the SIMD system would sequentially execute each possible instruction on the subset of processors whose next instruction match it. For a typical assembly language, even with a reduced instruction set, most processors would be idle most of the time. However, if the set of instructions implemented on the virtual processor is very small, this approach can be fruitful. In the case of Genetic Programming, the "instruction set" is composed of the specified set of functions designed for the task. We will show below that with a precompilation step, simply adding a push, a conditional, and unconditional branching and a stop instruction, we can get a very effective MIMD simulation running. | [
415,
2334
] | Train |
2,104 | 1 | Title: A study of the effects of group formation on evolutionary search
Abstract: As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system presented in [Koza and Andre 1995] is one of the rare such parallel implementations. Until today, no implementation has been proposed for parallel GP using a SIMD architecture, except for a data-parallel approach [Tufts 1995], although others have exploited workstation farms and pipelined supercomputers. One reason is certainly the apparent difficulty of dealing with the parallel evaluation of different S-expressions when only a single instruction can be executed at the same time on every processor. The aim of this chapter is to present such an implementation of parallel GP on a SIMD system, where each processor can efficiently evaluate a different S-expression. We have implemented this approach on a MasPar MP-2 computer, and will present some timing results. To the extent that SIMD machines, like the MasPar are available to offer cost-effective cycles for scientific experimentation, this is a useful approach. The idea of simulating a MIMD machine using a SIMD architecture is not new [Hillis and Steele 1986; Littman and Metcalf 1990; Dietz and Cohen 1992]. One of the original ideas for the Connection Machine [Hillis and Steele 1986] was that it could simulate other parallel architectures. Indeed, in the extreme, each processor on a SIMD architecture can simulate a universal Turing machine (TM). With different turing machine specifications stored in each local memory, each processor would simply have its own tape, tape head, state table and state pointer, and the simulation would be performed by repeating the basic TM operations simultaneously. Of course, such a simulation would be very inefficient, and difficult to program, but would have the advantage of being really MIMD, where no SIMD processor would be in idle state, until its simulated machine halts. Now let us consider an alternative idea, that each SIMD processor would simulate an individual stored program computer using a simple instruction set. For each step of the simulation, the SIMD system would sequentially execute each possible instruction on the subset of processors whose next instruction match it. For a typical assembly language, even with a reduced instruction set, most processors would be idle most of the time. However, if the set of instructions implemented on the virtual processor is very small, this approach can be fruitful. In the case of Genetic Programming, the "instruction set" is composed of the specified set of functions designed for the task. We will show below that with a precompilation step, simply adding a push, a conditional, and unconditional branching and a stop instruction, we can get a very effective MIMD simulation running. | [
403,
2302
] | Train |
2,105 | 2 | Title: A study of the effects of group formation on evolutionary search
Abstract: Information Processing in Primate Retinal Cone Pathways: Experiments and Results | [
2621
] | Test |
2,106 | 5 | Title: Theoretical Modeling of Superscalar Processor Performance
Abstract: The current trace-driven simulation approach to determine superscalar processor performance is widely used but has some shortcomings. Modern benchmarks generate extremely long traces, resulting in problems with data storage, as well as very long simulation run times. More fundamentally, simulation generally does not provide significant insight into the factors that determine performance or a characterization of their interactions. This paper proposes a theoretical model of superscalar processor performance that addresses these shortcomings. Performance is viewed as an interaction of program parallelism and machine parallelism. Both program and machine parallelisms are decomposed into multiple component functions. Methods for measuring or computing these functions are described. The functions are combined to provide a model of the interaction between program and machine parallelisms and an accurate estimate of the performance. The computed performance, based on this model, is compared to simulated performance for six benchmarks from the SPEC 92 suite on several configurations of the IBM RS/6000 instruction set architecture. | [
735,
1750,
2649
] | Validation |
2,107 | 2 | Title: Prediction of human mRNA donor and acceptor sites from the DNA sequence
Abstract: Artificial neural networks have been applied to the prediction of splice site location in human pre-mRNA. A joint prediction scheme where prediction of transition regions between introns and exons regulates a cutoff level for splice site assignment was able to predict splice site locations with confidence levels far better than previously reported in the literature. The problem of predicting donor and acceptor sites in human genes is hampered by the presence of numerous amounts of false positives | in the paper the distribution of these false splice sites is examined and linked to a possible scenario for the splicing mechanism in vivo. When the presented method detects 95% of the true donor and acceptor sites it makes less than 0.1% false donor site assignments and less than 0.4% false acceptor site assignments. For the large data set used in this study this means that on the average there are one and a half false donor sites per true donor site and six false acceptor sites per true acceptor site. With the joint assignment method more than a fifth of the true donor sites and around one fourth of the true acceptor sites could be detected without accompaniment of any false positive predictions. Highly confident splice sites could not be isolated with a widely used weight matrix method or by separate splice site networks. A complementary relation between the confidence levels of the coding/non-coding and the separate splice site networks was observed, with many weak splice sites having sharp transitions in the coding/non-coding signal and many stronger splice sites having more ill-defined transitions between coding and non-coding. | [
613,
1865,
1953,
2046,
2496,
2571,
2574
] | Test |
2,108 | 3 | Title: The Automated Mapping of Plans for Plan Recognition
Abstract: To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks. | [
1172,
1898,
2140
] | Train |
2,109 | 2 | Title: Local quartet splits of a binary tree infer all quartet splits via one dyadic inference
Abstract: DIMACS Technical Report 96-43 DIMACS is a partnership of Rutgers University, Princeton University, AT&T Research, Bellcore, and Bell Laboratories. DIMACS is an NSF Science and Technology Center, funded under contract STC-91-19999; and also receives support from the New Jersey Commission on Science and Technology. | [
2185
] | Train |
2,110 | 6 | Title: Constructing Big Trees from Short Sequences
Abstract: The construction of evolutionary trees is a fundamental problem in biology, and yet methods for reconstructing evolutionary trees are not reliable when it comes to inferring accurate topologies of large divergent evolutionary trees from realistic length sequences. We address this problem and present a new polynomial time algorithm for reconstructing evolutionary trees called the Short Quartets Method which is consistent and which has greater statistical power than other polynomial time methods, such as Neighbor-Joining and the 3-approximation algorithm by Agarwala et al. (and the "Double Pivot" variant of the Agarwala et al. algorithm by Cohen and Farach) for the L 1 -nearest tree problem. Our study indicates that our method will produce the correct topology from shorter sequences than can be guaranteed using these other methods. | [
299,
1827
] | Train |
2,111 | 1 | Title: Artificial Life as Theoretical Biology: How to do real science with computer simulation
Abstract: Artificial Life (A-Life) research offers, among other things, a new style of computer simulation for understanding biological systems and processes. But most current A-Life work does not show enough methodological sophistication to count as good theoretical biology. As a first step towards developing a stronger methodology for A-Life, this paper (1) identifies some methodological pitfalls arising from the `computer science inuence' in A-Life, (2) suggests some methodological heuristics for A-Life as theoretical biology, (3) notes the strengths of A-Life methods versus previous research methods in biology, (4) examines some open questions in theoretical biology that may benefit from A-Life simulation, and (5) argues that the debate over `Strong A-Life' is not relevant to A-Life's utility for theoretical biology. 1 Introduction: Simulating our way into the Dark Continent | [
2047,
2302
] | Train |
2,112 | 2 | Title: Approximation from shift-invariant subspaces of L
Abstract: A complete characterization is given of closed shift-invariant subspaces of L 2 (IR d ) which provide a specified approximation order. When such a space is principal (i.e., generated by a single function), then this characterization is in terms of the Fourier transform of the generator. As a special case, we obtain the classical Strang-Fix conditions, but without requiring the generating function to decay at infinity. The approximation order of a general closed shift-invariant space is shown to be already realized by a specifiable principal subspace. | [
365,
2572
] | Train |
2,113 | 6 | Title: Learning Model Bias minimum number of examples requred to learn a single task, and O(a
Abstract: In this paper the problem of learning appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks from the same domain, and a theorem is given bounding the number tasks that must be learnt. A corollary of the theorem is that if the tasks are known to possess a common internal representation or preprocessing then the number of examples required per task for good generalisation when learning n tasks simultaneously scales like O(a + b tive support for the theoretical results is reported. | [
2586,
2623
] | Train |
2,114 | 3 | Title: Probabilistic Principal Component Analysis
Abstract: Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA. | [
1923,
1928,
2124
] | Validation |
2,115 | 3 | Title: Modeling Belief in Dynamic Systems. Part I: Foundations
Abstract: Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations of belief change. We claim that a better understanding of belief change can be gained from examining appropriate semantic models. In this paper we propose a general framework in which to model belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes if he knows that is more plausible than :. We then consider some properties defining the interaction between knowledge and plausibility, and show how these properties affect the properties of belief. In particular, we show that by assuming two of the most natural properties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledge, plausibility (and hence belief), and time, which extends the framework of Halpern and Fagin for modeling knowledge in multi-agent systems. We then examine the problem of "minimal change". This notion can be captured by using prior plausibilities, an analogue to prior probabilities, which can be updated by "conditioning". We show by example that conditioning on a plausibility measure can capture many scenarios of interest. In a companion paper, we show how the two best-studied scenarios of belief change, belief revision and belief update, fit into our framework. ? Some of this work was done while both authors were at the IBM Almaden Research Center. The first author was also at Stanford while much of the work was done. IBM and Stanford's support are gratefully acknowledged. The work was also supported in part by the Air Force Office of Scientific Research (AFSC), under Contract F49620-91-C-0080 and grant F94620-96-1-0323 and by NSF under grants IRI-95-03109 and IRI-96-25901. A preliminary version of this paper appears in Proceedings of the 5th Conference on Theoretical Aspects of Reasoning About Knowledge, 1994, pp. 44-64, under the title "A knowledge-based framework for belief change, Part I: Foundations". | [
276,
2000
] | Train |
2,116 | 1 | Title: Differential Evolution A simple and efficient adaptive scheme for global optimization over continuous spaces
Abstract: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. | [
163,
1775,
2125
] | Train |
2,117 | 2 | Title: Stimulus specificity in perceptual learning: a consequence of experiments that are also stimulus specific? Keywords:
Abstract: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. | [
2639
] | Test |
2,118 | 4 | Title: On Step-Size and Bias in Temporal-Difference Learning
Abstract: We present results for three new algorithms for setting the step-size parameters, ff and , of temporal-difference learning methods such as TD(). The overall task is that of learning to predict the outcome of an unknown Markov chain based on repeated observations of its state trajectories. The new algorithms select step-size parameters online in such a way as to eliminate the bias normally inherent in temporal-difference methods. We compare our algorithms with conventional Monte Carlo methods. Monte Carlo methods have a natural way of setting the step size: for each state s they use a step size of 1=n s , where n s is the number of times state s has been visited. We seek and come close to achieving comparable step-size algorithms for TD(). One new algorithm uses a = 1=n s schedule to achieve the same effect as processing a state backwards with TD(0), but remains completely incremental. Another algorithm uses a at each time equal to the estimated transition probability of the current transition. We present empirical results showing improvement in convergence rate over Monte Carlo methods and conventional TD(). A limitation of our results at present is that they apply only to tasks whose state trajectories do not contain cycles. | [
565,
2442
] | Train |
2,119 | 2 | Title: Gas Identification System using Graded Temperature Sensor and Neural Net Interpretation
Abstract: We present results for three new algorithms for setting the step-size parameters, ff and , of temporal-difference learning methods such as TD(). The overall task is that of learning to predict the outcome of an unknown Markov chain based on repeated observations of its state trajectories. The new algorithms select step-size parameters online in such a way as to eliminate the bias normally inherent in temporal-difference methods. We compare our algorithms with conventional Monte Carlo methods. Monte Carlo methods have a natural way of setting the step size: for each state s they use a step size of 1=n s , where n s is the number of times state s has been visited. We seek and come close to achieving comparable step-size algorithms for TD(). One new algorithm uses a = 1=n s schedule to achieve the same effect as processing a state backwards with TD(0), but remains completely incremental. Another algorithm uses a at each time equal to the estimated transition probability of the current transition. We present empirical results showing improvement in convergence rate over Monte Carlo methods and conventional TD(). A limitation of our results at present is that they apply only to tasks whose state trajectories do not contain cycles. | [
2154
] | Train |
2,120 | 2 | Title: PUSH-PULL SHUNTING MODEL OF GANGLION CELLS Simulations of X and Y retinal ganglion cell behavior
Abstract: We present results for three new algorithms for setting the step-size parameters, ff and , of temporal-difference learning methods such as TD(). The overall task is that of learning to predict the outcome of an unknown Markov chain based on repeated observations of its state trajectories. The new algorithms select step-size parameters online in such a way as to eliminate the bias normally inherent in temporal-difference methods. We compare our algorithms with conventional Monte Carlo methods. Monte Carlo methods have a natural way of setting the step size: for each state s they use a step size of 1=n s , where n s is the number of times state s has been visited. We seek and come close to achieving comparable step-size algorithms for TD(). One new algorithm uses a = 1=n s schedule to achieve the same effect as processing a state backwards with TD(0), but remains completely incremental. Another algorithm uses a at each time equal to the estimated transition probability of the current transition. We present empirical results showing improvement in convergence rate over Monte Carlo methods and conventional TD(). A limitation of our results at present is that they apply only to tasks whose state trajectories do not contain cycles. | [
1798
] | Test |
2,121 | 2 | Title: Testing for Gaussianity and Non Linearity in the sustained portion of musical sounds.
Abstract: Higher order spectra of a signal contain information about the non Gaussian and non Linear properties of the system that created it. Since the non linearity in musical signal usually originate in the excitation signal while the linear spectral characteristics are attributed to the resonant chambers, we discard the spectral information by looking at the higher order statistical properties of the residual signal, i.e. the estimated input signal obtained by inverse filtering of the sound. In the current paper we show that the skewness and kurtosis values of the residual could be used for characterization of such important sound properties as belonging to families of strings, woodwind and brass instrumental timbres. The skewness parameter is shown to be closely related to the bicoherence function calculated over the original signal and as such it is succinct to an interpretation as statistical test for the signal conforming to a linear non Gaussian model. The above results are compared to the Hinich bispectral tests for Gaussianity and non Linearity of time series and exhibit a similar classification results. Finally, regarding the higher order statistics of a signal as a feature vector, a statistical distance measure for the cumulant space is suggested. | [
2212
] | Test |
2,122 | 0 | Title: Preparing Case Retrieval Nets for Distributed Processing
Abstract: In this paper, we discuss two approaches of applying the memory model of Case Retrieval Nets to applications where a distributed processing of information is required. For this, we distinguish two types of such applications, namely (a) the case of distributed case libraries and (b) the case of distributed cases. While a solution to the former is straightforward, the latter requires an extension to Case Retrieval Nets which provides a kind of partitioning of the entire net structure. This extended model even allows for a concurrent implementation of the retrieval process or for the use of collaborative agents for retrieval. Keywords: Case-based reasoning, case retrieval, memory structures, distributed processing. | [
66,
75,
1854,
1855,
1864
] | Validation |
2,123 | 0 | Title: Justification Structures for Document Reuse
Abstract: Document drafting|an important problem-solving task of professionals in a wide variety of fields|typifies a design task requiring complex adaptation for case reuse. This paper proposes a framework for document reuse based on an explicit representation of the illocutionary and rhetorical structure underlying documents. Explicit representation of this structure facilitates (1) interpretation of previous documents by enabling them to "explain themselves," (2) construction of documents by enabling document drafters to issue goal-based specifications and rapidly retrieve documents with similar intentional structure, and (3) mainte nance of multi-generation documents. | [
649,
2482
] | Train |
2,124 | 2 | Title: A Hierarchical Latent Variable Model for Data Visualization
Abstract: Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multi-variate data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines, and to data in 36 dimensions derived from satellite images. A Matlab software implementation of the algorithm is publicly available from the world-wide web. | [
74,
1928,
2114
] | Train |
2,125 | 2 | Title: On the Usage of Differential Evolution for Function Optimization Differential Evolution (DE) has recently proven
Abstract: assumed unless otherwise stated. Basically, DE generates new parameter vectors by adding the weighted difference between two population vectors to a third vector. If the resulting vector yields a lower objective function value than a predetermined population member, the newly generated vector replaces the vector, with which it was compared, in the next generation; otherwise, the old vector is retained. This basic principle, however, is extended when it comes to the practical variants of DE. For example an existing vector can be perturbed by adding more than one weighted difference vector to it. In most cases, it is also worthwhile to mix the parameters of the old vector with those of the perturbed one before comparing the objective function values. Several variants of DE which have proven to be useful will be described in the | [
2116
] | Train |
2,126 | 5 | Title: Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra
Abstract: We present a novel application of ILP to the problem of diterpene structure elucidation from 13 C NMR spectra. Diterpenes are organic compounds of low molecular weight that are based on a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical effectors. The structure elucidation of diterpenes based on 13 C NMR spectra is usually done manually by human experts with specialized background knowledge on peak patterns and chemical structures. In the process, each of the 20 skeletal atoms is assigned an atom number that corresponds to its proper place in the skeleton and the diterpene is classified into one of the possible skeleton types. We address the problem of learning classification rules from a database of peak patterns for diterpenes with known structure. Recently, propositional learning was successfully applied to learn classification rules from spectra with assigned atom numbers. As the assignment of atom numbers is a difficult process in itself (and possibly indistinguishable from the classification process), we apply ILP, i.e., relational learning, to the problem of classifying spectra without assigned atom numbers. | [
426,
2213,
2339,
2426,
2591
] | Train |
2,127 | 3 | Title: NAIVE BAYESIAN LEARNING Adapted from
Abstract: We present a novel application of ILP to the problem of diterpene structure elucidation from 13 C NMR spectra. Diterpenes are organic compounds of low molecular weight that are based on a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical effectors. The structure elucidation of diterpenes based on 13 C NMR spectra is usually done manually by human experts with specialized background knowledge on peak patterns and chemical structures. In the process, each of the 20 skeletal atoms is assigned an atom number that corresponds to its proper place in the skeleton and the diterpene is classified into one of the possible skeleton types. We address the problem of learning classification rules from a database of peak patterns for diterpenes with known structure. Recently, propositional learning was successfully applied to learn classification rules from spectra with assigned atom numbers. As the assignment of atom numbers is a difficult process in itself (and possibly indistinguishable from the classification process), we apply ILP, i.e., relational learning, to the problem of classifying spectra without assigned atom numbers. | [
1329,
2338
] | Train |
2,128 | 0 | Title: Intelligent Model Selection for Hillclimbing Search in Computer-Aided Design
Abstract: Models of physical systems can differ according to computational cost, accuracy and precision, among other things. Depending on the problem solving task at hand, different models will be appropriate. Several investigators have recently developed methods of automatically selecting among multiple models of physical systems. Our research is novel in that we are developing model selection techniques specifically suited to computer-aided de sign. Our approach is based on the idea that artifact performance models for computer-aided design should be chosen in light of the design decisions they are required to support. We have developed a technique called "Gradient Magnitude Model Selection" (GMMS), which embodies this principle. GMMS operates in the context of a hillclimbing search process. It selects the simplest model that meets the needs of the hillclimbing algorithm in which it operates. We are using the domain of sailing yacht design as a testbed for this research. We have implemented GMMS and used it in hillclimb-ing search to decide between a computationally expensive potential-flow program and an algebraic approximation to analyze the performance of sailing yachts. Experimental tests show that GMMS makes the design process faster than it would be if the most expensive model were used for all design evaluations. GMMS achieves this performance improvement with little or no sacrifice in the quality of the resulting design. | [
2030,
2479
] | Train |
2,129 | 2 | Title: Fast Pruning Using Principal Components
Abstract: We present a new algorithm for eliminating excess parameters and improving network generalization after supervised training. The method, "Principal Components Pruning (PCP)", is based on principal component analysis of the node activations of successive layers of the network. It is simple, cheap to implement, and effective. It requires no network retraining, and does not involve calculating the full Hessian of the cost function. Only the weight and the node activity correlation matrices for each layer of nodes are required. We demonstrate the efficacy of the method on a regression problem using polynomial basis functions, and on an economic time series prediction problem using a two-layer, feedforward network. | [
2454
] | Train |
2,130 | 1 | Title: Intelligent Gradient-Based Search of Incompletely Defined Design Spaces
Abstract: Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions. We present a rule-based technique for intelligently computing gradients in the presence of such pathologies in the simulators, and show how this gradient computation method can be used as part of a gradient-based numerical optimization system. We tested the resulting system in the domain of conceptual design of supersonic transport aircraft, and found that using rule-based gradients can decrease the cost of design space search by one or more orders of magnitude. | [
2030
] | Validation |
2,131 | 0 | Title: Learning Prototype-Selection Rules for Case-Based Iterative Design seen as a case-based reasoning system [4], in
Abstract: The first step for most case-based design systems is to select an initial prototype from a database of previous designs. The retrieved prototype is then modified to tailor it to the given goals. For any particular design goal the selection of a starting point for the design process can have a dramatic effect both on the quality of the eventual design and on the overall design time. We present a technique for automatically constructing effective prototype-selection rules. Our technique applies a standard inductive-learning algorithm, C4.5, to a set of training data describing which particular prototype would have been the best choice for each goal encountered in a previous design session. We have tested our technique in the domain of racing-yacht-hull design, comparing our inductively learned selection rules to several competing prototype-selection methods. Our results show that the inductive prototype-selection method leads to better final designs when the design process is guided by a noisy evaluation function, and that the inductively learned rules will often be more efficient than competing methods. Many automated design systems begin by retrieving an initial prototype from a library of previous designs, using the given design goal as an index to guide the retrieval process [14]. The retrieved prototype is then modified by a set of design modification operators to tailor the selected design to the given goals. In many cases the quality of competing designs can be assessed using domain-specific evaluation functions, and in such cases the design-modification process is often This research has benefited from numerous discussions with members of the Rutgers CAP project. We thank Andrew Gelsey for helping with the cross-validation code, John Keane for helping with RUVPP, and Andrew Gelsey and Tim Weinrich for comments on a previous draft of this paper. This research was supported under ARPA-funded NASA grant NAG 2-645. In the context of such case-based design systems, the choice of an initial prototype can affect both the quality of the final design and the computational cost of obtaining that design, for three reasons. First, prototype selection may impact quality when the prototypes lie in disjoint search spaces. In particular, if the system's design modification operators cannot convert any prototype into any other prototype, the choice of initial prototype will restrict the set of possible designs that can be obtained by any search process. A poor choice of initial prototype may therefore lead to a suboptimal final design. Second, prototype selection may impact quality when the design process is guided by a nonlinear evaluation function with unknown global properties. Since there is no known method that is guaranteed to find the global optimum of an arbitrary nonlinear function [7], most design systems rely on iterative local search methods whose results are sensitive to the initial starting point. Finally, the choice of prototype may have an impact on the time needed to carry out the design modification process|two different starting points may yield the same final design but take very different amounts of time to get there. In design problems where evaluating even just a single design can take tremendous amounts of time, selecting an appropriate initial prototype can be the determining factor in the success or failure of the design process. This paper describes the application of inductive learning [11] to form rules for selecting appropriate prototype designs. The paper is structured as follows. In Section 2, we describe our inductive method for learning prototype-selection rules. In Section 3 we describe the domain of racing-yacht-hull design, in which we tested our prototype-selection methods. In Sections 4 and 5, we describe the experiments | [
1892,
2030,
2319
] | Validation |
2,132 | 5 | Title: Combining Data Mining and Machine Learning for Effective User Profiling
Abstract: This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rule- learning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud. | [
382,
1837
] | Validation |
2,133 | 1 | Title: Genetic Programming Bloat with Dynamic Fitness
Abstract: Technical Report: CSRP-97-29, 3 December 1997 Abstract In artificial evolution individuals which perform as their parents are usually rewarded identically to their parents. We note that Nature is more dynamic and there may be a penalty to pay for doing the same thing as your parents. We report two sets of experiments where static fitness functions are firstly augmented by a penalty for unchanged offspring and secondly the static fitness case is replaced by randomly generated dynamic test cases. We conclude genetic programming, when evolving artificial ant control programs, is surprisingly little effected by large penalties and program growth is observed in all our experiments. | [
1925,
2199,
2206
] | Train |
2,134 | 6 | Title: Learning to Classify Sensor Data inductive bias, supervised Bayesian learning, minimum description length.
Abstract: | [
2644
] | Train |
2,135 | 2 | Title: Learning Polynomial Functions by Feature Construction
Abstract: We present a method for learning higher-order polynomial functions from examples using linear regression and feature construction. Regression is used on a set of training instances to produce a weight vector for a linear function over the feature set. If this hypothesis is imperfect, a new feature is constructed by forming the product of the two features that most effectively predict the squared error of the current hypothesis. The algorithm is then repeated. In an extension to this method, the specific pair of features to combine is selected by measuring their joint ability to predict the hypothesis' error. | [
134,
2012,
2023,
2333,
2583
] | Test |
2,136 | 3 | Title: Bayesian Experimental Design: A Review
Abstract: Non Bayesian experimental design for linear models has been reviewed by Stein-berg and Hunter (1984) and in the recent book by Pukelsheim (1993); Ford, Kitsos and Titterington (1989) reviewed non Bayesian design for nonlinear models. Bayesian design for both linear and nonlinear models is reviewed here. We argue that the design problem is best considered as a decision problem and that it is best solved by maximizing the expected utility of the experiment. This paper considers only in a marginal way, when appropriate, the theory of non Bayesian design. | [
2148
] | Train |
2,137 | 0 | Title: Search-based Class Discretization
Abstract: We present a methodology that enables the use of classification algorithms on regression tasks. We implement this method in system RECLA that transforms a regression problem into a classification one and then uses an existent classification system to solve this new problem. The transformation consists of mapping a continuous variable into an ordinal variable by grouping its values into an appropriate set of intervals. We use misclassification costs as a means to reflect the implicit ordering among the ordinal values of the new variable. We describe a set of alternative discretization methods and, based on our experimental results, justify the need for a search-based approach to choose the best method. Our experimental results confirm the validity of our search-based approach to class discretization, and reveal the accuracy benefits of adding misclassification costs. | [
430,
431,
2508
] | Train |
2,138 | 3 | Title: A Nonparametric Bayesian Approach to Modelling Nonlinear Time Series
Abstract: The Bayesian multivariate adaptive regression spline (BMARS) methodology of Denison et al. (1997) is extended to cope with nonlinear time series and financial datasets. The nonlinear time series model is closely related to the adaptive spline threshold autoregressive (ASTAR) method of Lewis and Stevens (1991) while the financial models can be thought of as Bayesian versions of both the generalised and simple autoregressive conditional het-eroscadastic (GARCH and ARCH) models. | [
1718,
2285
] | Train |
2,139 | 1 | Title: Evolving Teamwork and Coordination with Genetic Programming
Abstract: Some problems can be solved only by multi-agent teams. In using genetic programming to produce such teams, one faces several design decisions. First, there are questions of team diversity and of breeding strategy. In one commonly used scheme, teams consist of clones of single individuals; these individuals breed in the normal way and are cloned to form teams during fitness evaluation. In contrast, teams could also consist of distinct individuals. In this case one can either allow free interbreeding between members of different teams, or one can restrict interbreeding in various ways. A second design decision concerns the types of coordination-facilitating mechanisms provided to individual team members; these range from sensors of various sorts to complex communication systems. This paper examines three breeding strategies (clones, free, and restricted) and three coordination mechanisms (none, deictic sensing, and name-based sensing) for evolving teams of agents in the Serengeti world, a simple predator/prey environment. Among the conclusions are the fact that a simple form of restricted interbreeding outperforms free interbreeding in all teams with distinct individuals, and the fact that name-based sensing consistently outperforms deictic sensing. | [
995,
2220,
2226
] | Test |
2,140 | 3 | Title: Sonderforschungsbereich 314 K unstliche Intelligenz Wissensbasierte Systeme KI-Labor am Lehrstuhl f ur Informatik IV Numerical
Abstract: Some problems can be solved only by multi-agent teams. In using genetic programming to produce such teams, one faces several design decisions. First, there are questions of team diversity and of breeding strategy. In one commonly used scheme, teams consist of clones of single individuals; these individuals breed in the normal way and are cloned to form teams during fitness evaluation. In contrast, teams could also consist of distinct individuals. In this case one can either allow free interbreeding between members of different teams, or one can restrict interbreeding in various ways. A second design decision concerns the types of coordination-facilitating mechanisms provided to individual team members; these range from sensors of various sorts to complex communication systems. This paper examines three breeding strategies (clones, free, and restricted) and three coordination mechanisms (none, deictic sensing, and name-based sensing) for evolving teams of agents in the Serengeti world, a simple predator/prey environment. Among the conclusions are the fact that a simple form of restricted interbreeding outperforms free interbreeding in all teams with distinct individuals, and the fact that name-based sensing consistently outperforms deictic sensing. | [
623,
1268,
1898,
2108,
2292
] | Train |
2,141 | 6 | Title: Fast and Simple Algorithms for Perfect Phylogeny and Triangulating Colored Graphs
Abstract: This paper presents an O((r n=m) m rnm) algorithm for determining whether a set of n species has a perfect phylogeny, where m is the number of characters used to describe a species and r is the maximum number of states that a character can be in. The perfect phylogeny algorithm leads to an O((2e=k) k e 2 k) algorithm for triangulating a k-colored graph having e edges. | [
2418,
2511
] | Test |
2,142 | 5 | Title: Run-time versus Compile-time Instruction Scheduling in Superscalar (RISC) Processors: Performance and Tradeoffs
Abstract: The RISC revolution has spurred the development of processors with increasing levels of instruction level parallelism (ILP). In order to realize the full potential of these processors, multiple instructions must be issued and executed in a single cycle. Consequently, instruction scheduling plays a crucial role as an optimization in this context. While early attempts at instruction scheduling were limited to compile-time approaches, the recent trend is to provide dynamic support in hardware. In this paper, we present the results of a detailed comparative study of the performance advantages to be derived by the spectrum of instruction scheduling approaches: from limited basic-block schedulers in the compiler, to novel and aggressive run-time schedulers in hardware. A significant portion of our experimental study via simulations, is devoted to understanding the performance advantages of run-time scheduling. Our results indicate it to be effective in extracting the ILP inherent to the program trace being scheduled, over a wide range of machine and program parameters. Furthermore, we also show that this effectiveness can be further enhanced by a simple basic-block scheduler in the compiler, which optimizes for the presence of the run-time scheduler in the target; current basic-block schedulers are not designed to take advantage of this feature. We demonstrate this fact by presenting a novel enhanced basic-block scheduler in this paper. Finally, we outline a simple analytical characterization of the performance advantage, that run-time schedulers have to offer. | [
2096
] | Train |
2,143 | 2 | Title: MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS
Abstract: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-temporal resolutions; (b) lack of accuracy of the experimental paradigm used for EEG complementary studies. Images (Posner & Raichle, 1994) is an excellent introduction to interdisciplinary research in cognitive and imaging science. Well written and illustrated, it presents concepts in a manner well suited both to the layman/undergraduate and to the technical nonexpert/graduate student and postdoctoral researcher. Many, not all, people involved in interdisciplinary neuroscience research agree with the P & R's statements on page 33, on the importance of recognizing emergent properties of brain function from assemblies of neurons. It is clear from the sparse references that this book was not intended as a standalone review of a broad field. There are some aws in the scientific development, but this must be expected in such a pioneering venture. P & R hav e proposed many cognitive mechanisms deserving further study with imaging tools yet to be developed which can yield better spatial-temporal resolutions. | [
2181
] | Train |
2,144 | 3 | Title: UNIVERSAL FORMULAS FOR TREATMENT EFFECTS FROM NONCOMPLIANCE DATA
Abstract: This paper establishes formulas that can be used to bound the actual treatment effect in any experimental study in which treatment assignment is random but subject compliance is imperfect. These formulas provide the tightest bounds on the average treatment effect that can be inferred given the distribution of assignments, treatments, and responses. Our results reveal that even with high rates of noncompliance, experimental data can yield significant and sometimes accurate information on the effect of a treatment on the population. | [
1326,
1894
] | Train |
2,145 | 6 | Title: Exploration in Machine Learning
Abstract: Most researchers in machine learning have built their learning systems under the assumption that some external entity would do all the work of furnishing the learning experiences. Recently, however, investigators in several subfields of machine learning have designed systems that play an active role in choosing the situations from which they will learn. Such activity is generally called exploration. This paper describes a few of these exploratory learning projects, as reported in the literature, and attempts to extract a general account of the issues involved in exploration. | [
2408
] | Validation |
2,146 | 6 | Title: On Learning Read-k-Satisfy-j DNF
Abstract: We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by k, and the number of terms satisfied by any assignment is at most j. After motivating the investigation of this class of DNF formulas, we present an algorithm that with high probability finds a DNF formula that is logically equivalent to any unknown RkSj DNF formula to be learned. The algorithm uses the well-studied protocol of equivalence and membership queries, and runs in polynomial time for k j = O( log n log log n ), where n is the number of input variables. | [
638,
1003,
1004,
1897,
2182
] | Test |
2,147 | 2 | Title: Extraction of Facial Features for Recognition using Neural Networks
Abstract: | [
2019,
2498,
2499
] | Train |
2,148 | 3 | Title: Bayesian Design for the Normal Linear Model with Unknown Error Variance
Abstract: Most of the Bayesian theory of optimal experimental design, for the normal linear model, has been developed under the restrictive assumption that the variance is known. In special cases, insensitivity of specific design criteria to specific prior assumptions on the variance has been demonstrated, but a general result to show the way in which Bayesian optimal designs are affected by prior information about the variance is lacking. This paper stresses the important distinction between expected utility functions and optimality criteria, examines a number of expected utility functions some of which possess interesting properties, and deserve wider use and derives the relevant Bayesian optimality criteria under normal assumptions. This unifying setup is useful for proving the main result of the paper, that clarifies the issue of designing for the normal linear model with unknown variance. | [
2136
] | Validation |
2,149 | 5 | Title: Scheduling and Mapping: Software Pipelining in the Presence of Structural Hazards proposed formulation and a
Abstract: Recently, software pipelining methods based on an ILP (Integer Linear Programming) framework have been successfully applied to derive rate-optimal schedules for architectures involving clean pipelines | pipelines without structural hazards. The problem for architectures beyond such clean pipelines remains open. One challenge is how, under a unified ILP framework, to simultaneously represent resource constraints for unclean pipelines, and the assignment or mapping of operations from a loop to those pipelines. In this paper we provide a framework which does exactly this, and in addition constructs rate-optimal software pipelined schedules. | [
1955,
2188,
2190,
2194
] | Train |
2,150 | 4 | Title: Multi-time Models for Temporally Abstract Planning
Abstract: Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an approach to this problem based on the mathematical framework of Markov decision processes and reinforcement learning. Current model-based reinforcement learning is based on one-step models that cannot represent common-sense higher-level actions, such as going to lunch, grasping an object, or flying to Denver. This paper generalizes prior work on temporally abstract models [Sutton, 1995] and extends it from the prediction setting to include actions, control, and planning. We introduce a more general form of temporally abstract model, the multi-time model, and establish its suitability for planning and learning by virtue of its relationship to the Bellman equations. This paper summarizes the theoretical framework of multi-time models and illustrates their potential advantages in a The need for hierarchical and abstract planning is a fundamental problem in AI (see, e.g., Sacerdoti, 1977; Laird et al., 1986; Korf, 1985; Kaelbling, 1993; Dayan & Hinton, 1993). Model-based reinforcement learning offers a possible solution to the problem of integrating planning with real-time learning and decision-making (Peng & Williams, 1993, Moore & Atkeson, 1993; Sutton and Barto, 1998). However, current model-based reinforcement learning is based on one-step models that cannot represent common-sense, higher-level actions. Modeling such actions requires the ability to handle different, interrelated levels of temporal abstraction. A new approach to modeling at multiple time scales was introduced by Sutton (1995) based on prior work by Singh , Dayan , and Sutton and Pinette . This approach enables models of the environment at different temporal scales to be intermixed, producing temporally abstract models. However, that work was concerned only with predicting the environment. This paper summarizes an extension of the approach including actions and control of the environment [Precup & Sutton, 1997]. In particular, we generalize the usual notion of a gridworld planning task. | [
1192,
1954,
2179,
2183,
2222,
2305
] | Test |
2,151 | 0 | Title: A Yardstick for the Evaluation of Case-Based Classifiers
Abstract: This paper proposes that the generalisation capabilities of a case-based reasoning system can be evaluated by comparison with a `rote-learning' algorithm which uses a very simple generalisation strategy. Two such algorithms are defined, and expressions for their classification accuracy are derived as a function of the size of training sample. A series of experiments using artificial and `natural' data sets is described in which the learning curve for a case-based learner is compared with those for the apparently trivial rote-learning learning algorithms. The results show that in a number of `plausible' situations, the learning curves for a simple case-based learner and the `majority' rote-learner can barely be distinguished, although a domain is demonstrated where favourable performance from the case-based learner is observed. This suggests that the maxim of case-based reasoning that `similar problems have similar solutions' may be useful as the basis of a generalisation strategy only in selected domains. | [
1109,
1584,
2037,
2342
] | Validation |
2,152 | 1 | Title: Cellular Encoding for Interactive Evolutionary Robotics
Abstract: Research in robotics programming is divided in two camps. The direct hand programmming approach uses an explicit model or a behavioral model ( subsumption architecture). The machine learning community uses neural network and/or genetic algorithm. We claim that hand programming and learning are complementary. The two approaches used together can be orders of magnitude more powerful than each approach taken separately. We propose a method to combine them both. It includes three concepts : syntactic constraints to restrict the search space, hand-made problem decomposition, hand given fitness. We use this method to solve a complex problem ( eight-legged locomotion). It needs 5000 less evaluations compared to when genetic algorithm are used alone. | [
1277,
2429
] | Test |
2,153 | 3 | Title: Rates of convergence of the Hastings and Metropolis algorithms
Abstract: We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either independent or symmetric candidate distributions, and provide necessary and sufficient conditions for the algorithms to converge at a geometric rate to a prescribed distribution . In the independence case (in IR k ) these indicate that geometric convergence essentially occurs if and only if the candidate density is bounded below by a multiple of ; in the symmetric case (in IR only) we show geometric convergence essentially occurs if and only if has geometric tails. We also evaluate recently developed computable bounds on the rates of convergence in this context: examples show that these theoretical bounds can be inherently extremely conservative, although when the chain is stochastically monotone the bounds may well be effective. | [
115,
889,
1713,
1716,
1977,
1982,
1992,
2002,
2008,
2022,
2025,
2219,
2318,
2699
] | Train |
2,154 | 2 | Title: Olfaction Metal Oxide Semiconductor Gas Sensors and Neural Networks
Abstract: We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either independent or symmetric candidate distributions, and provide necessary and sufficient conditions for the algorithms to converge at a geometric rate to a prescribed distribution . In the independence case (in IR k ) these indicate that geometric convergence essentially occurs if and only if the candidate density is bounded below by a multiple of ; in the symmetric case (in IR only) we show geometric convergence essentially occurs if and only if has geometric tails. We also evaluate recently developed computable bounds on the rates of convergence in this context: examples show that these theoretical bounds can be inherently extremely conservative, although when the chain is stochastically monotone the bounds may well be effective. | [
2119
] | Test |
2,155 | 2 | Title: Cognitive Computation (Extended Abstract)
Abstract: Cognitive computation is discussed as a discipline that links together neurobiology, cognitive psychology and artificial intelligence. | [
591,
2467
] | Train |
2,156 | 6 | Title: WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS
Abstract: We consider the game of sequentially assigning probabilities to future data based on past observations under logarithmic loss. We are not making probabilistic assumptions about the generation of the data, but consider a situation where a player tries to minimize his loss relative to the loss of the (with hindsight) best distribution from a target class for the worst sequence of data. We give bounds on the minimax regret in terms of the metric entropies of the target class with respect to suitable distances between distributions. | [
453,
2098
] | Train |
2,157 | 0 | Title: Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures
Abstract: In [9] we introduced a formal framework for constructing ordinal similarity measures, and suggested how this might also be applied to cardinal measures. In this paper we will place this approach in a more general framework, called similarity metrics. In this framework, ordinal similarity metrics (where comparison returns a boolean value) can be combined with cardinal metrics (returning a numeric value) and, indeed, with metrics returning values of other types, to produce new metrics. | [
66,
288,
2565
] | Train |
2,158 | 5 | Title: Learning Recursion with Iterative Bootstrap Induction (Extended Abstract)
Abstract: In this paper we are concerned with the problem of inducing recursive Horn clauses from small sets of training examples. The method of iterative bootstrap induction is presented. In the first step, the system generates simple clauses, which can be regarded as properties of the required definition. Properties represent generalizations of the positive examples, simulating the effect of having larger number of examples. Properties are used subsequently to induce the required recursive definitions. This paper describes the method together with a series of experiments. The results support the thesis that iterative bootstrap induction is indeed an effective technique that could be of general use in ILP. | [
1498,
2449
] | Validation |
2,159 | 3 | Title: Wavelet Shrinkage: Asymptopia?
Abstract: Considerable effort has been directed recently to develop asymptotically minimax methods in problems of recovering infinite-dimensional objects (curves, densities, spectral densities, images) from noisy data. A rich and complex body of work has evolved, with nearly- or exactly- minimax estimators being obtained for a variety of interesting problems. Unfortunately, the results have often not been translated into practice, for a variety of reasons sometimes, similarity to known methods, sometimes, computational intractability, and sometimes, lack of spatial adaptivity. We discuss a method for curve estimation based on n noisy data; one translates the empirical wavelet coefficients towards the origin by an amount method is different from methods in common use today, is computationally practical, and is spatially adaptive; thus it avoids a number of previous objections to minimax estimators. At the same time, the method is nearly minimax for a wide variety of loss functions - e.g. pointwise error, global error measured in L p norms, pointwise and global error in estimation of derivatives and for a wide range of smoothness classes, including standard Holder classes, Sobolev classes, and Bounded Variation. This is a much broader near-optimality than anything previously proposed in the minimax literature. Finally, the theory underlying the method is interesting, as it exploits a correspondence between statistical questions and questions of optimal recovery and information-based complexity. Acknowledgements: These results have been described at the Oberwolfach meeting `Mathematische Stochastik' December, 1992 and at the AMS Annual meeting, January 1993. This work was supported by NSF DMS 92-09130. The authors would like to thank Paul-Louis Hennequin, who organized the Ecole d' Ete de Probabilites at Saint Flour 1990, where this collaboration began, and to Universite de Paris VII (Jussieu) and Universite de Paris-sud (Orsay) for supporting visits of DLD and IMJ. The authors would like to thank Ildar Ibragimov and Arkady Nemirovskii for personal correspondence cited below. p | [
1910,
2081,
2661
] | Test |
2,160 | 3 | Title: group, and despite having just 337 subjects, the study strongly supports Identification of causal effects
Abstract: Figure 8a and Figure 8b show the prior distribution over f(-CR ) that follows from the flat prior and the skewed prior, respectively. Figure 8c and Figure 8d show the posterior distribution p(f (-CR jD)) obtained by our system when run on the Lipid data, using the flat prior and the skewed prior, respectively. From the bounds of Balke and Pearl (1994), it follows that under the large-sample assumption, 0:51 f (-CR jD) 0:86. Figure 8: Prior (a, b) and posterior (c,d) distributions for a subpopulation f (-CR jD) specified by the counter-factual query "Would Joe have improved had he taken the drug, given that he did not improve without it". (a) corresponds to the flat prior, (b) to the skewed prior. This paper identifies and demonstrates a new application area for network-based inference techniques - the management of causal analysis in clinical experimentation. These techniques, which were originally developed for medical diagnosis, are shown capable of circumventing one of the major problems in clinical experiments the assessment of treatment efficacy in the face of imperfect compliance. While standard diagnosis involves purely probabilistic inference in fully specified networks, causal analysis involves partially specified networks in which the links are given causal interpretation and where the domain of some variables are unknown. The system presented in this paper provides the clinical research community, we believe for the first time, an assumption-free, unbiased assessment of the average treatment effect. We offer this system as a practical tool to be used whenever full compliance cannot be enforced and, more broadly, whenever the data available is insufficient for answering the queries of interest to the clinical investigator. Lipid Research Clinic Program. 1984. The lipid research clinics coronary primary prevention trial results, parts i and ii. Journal of the American Medical Association 251(3):351-374. January. | [
1747,
2434
] | Train |
2,161 | 3 | Title: On The Foundation Of Structural Equation Models or
Abstract: When Can We Give Causal Interpretation Abstract The assumptions underlying statistical estimation are of fundamentally different character from the causal assumptions that underly structural equation models (SEM). The differences have been blurred through the years for the lack of a mathematical notation capable of distinguishing causal from equational relationships. Recent advances in graphical methods provide formal explication of these differences, and are destined to have profound impact on SEM's practice and philosophy. | [
1326,
2167
] | Train |
2,162 | 2 | Title: Incremental Class Learning approach and its application to Handwritten Digit Recognition
Abstract: Incremental Class Learning (ICL) provides a feasible framework for the development of scalable learning systems. Instead of learning a complex problem at once, ICL focuses on learning subproblems incrementally, one at a time | using the results of prior learning for subsequent learning | and then combining the solutions in an appropriate manner. With respect to multi-class classification problems, the ICL approach presented in this paper can be summarized as follows. Initially the system focuses on one category. After it learns this category, it tries to identify a compact subset of features (nodes) in the hidden layers, that are crucial for the recognition of this category. The system then freezes these crucial nodes (features) by fixing their incoming weights. As a result, these features cannot be obliterated in subsequent learning. These frozen features are available during subsequent learning and can serve as parts of weight structures build to recognize other categories. As more categories are learned, the set of features gradually stabilizes and learning a new category requires less effort. Eventually, learning a new category may only involve combining existing features in an appropriate manner. The approach promotes the sharing of learned features among a number of categories and also alleviates the well-known catastrophic interference problem. We present results of applying the ICL approach to the Handwritten Digit Recognition problem, based on a spatio-temporal representation of patterns. | [
745,
2586,
2599
] | Validation |
2,163 | 5 | Title: Speculative Hedge: Regulating Compile-Time Speculation Against Profile Variations code performance in the presence of execution
Abstract: Path-oriented scheduling methods, such as trace scheduling and hyperblock scheduling, use speculation to extract instruction-level parallelism from control-intensive programs. These methods predict important execution paths in the current scheduling scope using execution profiling or frequency estimation. Aggressive speculation is then applied to the important execution paths, possibly at the cost of degraded performance along other paths. Therefore, the speed of the output code can be sensitive to the compiler's ability to accurately predict the important execution paths. Prior work in this area has utilized the speculative yield function by Fisher, coupled with dependence height, to distribute instruction priority among execution paths in the scheduling scope. While this technique provides more stability of performance by paying attention to the needs of all paths, it does not directly address the problem of mismatch between compile-time prediction and run-time behavior. The work presented in this paper extends the speculative yield and dependence height heuristic to explicitly minimize the penalty suffered by other paths when instructions are speculated along a path. Since the execution time of a path is determined by the number of cycles spent between a path's entrance and exit in the scheduling scope, the heuristic attempts to eliminate unnecessary speculation that delays any path's exit. Such control of speculation makes the performance much less sensitive to the actual path taken at run time. The proposed method has a strong emphasis on achieving minimal delay to all exits. Thus the name, speculative hedge, is used. This paper presents the speculative hedge heuristic, and shows how it controls over-speculation in a superblock/hyperblock scheduler. The stability of out Copyright 1996 IEEE. Published in the Proceedings of the 29th Annual International Symposium on Microarchitecture, De-cember 2-4, 1996, Paris, France. Personal use of this material is permitted. However, permission to reprint/republish this material for resale or redistribution purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966 | [
1849
] | Test |
2,164 | 3 | Title: Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem
Abstract: A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. The techniques used in these algorithms are closely related to network structures and some of them are not easy to understand and implement. In this paper, we consider the problem from the combinatorial optimization point of view and state that efficient probabilistic inference in a belief network is a problem of finding an optimal factoring given a set of probability distributions. From this viewpoint, previously developed algorithms can be seen as alternate factoring strategies. In this paper, we define a combinatorial optimization problem, the optimal factoring problem, and discuss application of this problem in belief networks. We show that optimal factoring provides insight into the key elements of efficient probabilistic inference, and demonstrate simple, easily implemented algorithms with excellent performance. | [
5,
515,
1749,
2094,
2521
] | Train |
2,165 | 1 | Title: Auto-teaching: networks that develop their own teaching input
Abstract: Backpropagation learning (Rumelhart, Hinton and Williams, 1986) is a useful research tool but it has a number of undesiderable features such as having the experimenter decide from outside what should be learned. We describe a number of simulations of neural networks that internally generate their own teaching input. The networks generate the teaching input by trasforming the network input through connection weights that are evolved using a form of genetic algorithm. What results is an innate (evolved) capacity not to behave efficiently in an environment but to learn to behave efficiently. The analysis of what these networks evolve to learn shows some interesting results. | [
129,
538,
745,
1143,
2193,
2363
] | Train |
2,166 | 3 | Title: Probabilistic evaluation of counterfactual queries
Abstract: To appear in the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, July 31 August 4, 1994. Technical Report R-213-A April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted as an external action that forces the proposition A to be true, which is consistent with Lewis' Miraculous Analysis. This formalism offers a concrete embodiment of the "closest world" approach which (1) properly reflects common understanding of causal influences, (2) deals with the uncertainties inherent in the world, and (3) is amenable to machine representation. | [
260,
772,
776,
971,
1527,
2167,
2524
] | Train |
2,167 | 3 | Title: Counterfactuals and Policy Analysis in Structural Models
Abstract: Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, determination of liability, and policy analysis. We present a method for evaluating counter-factuals when the underlying causal model is represented by structural models a nonlinear generalization of the simultaneous equations models commonly used in econometrics and social sciences. This new method provides a coherent means for evaluating policies involving the control of variables which, prior to enacting the policy were influenced by other variables in the system. | [
772,
2088,
2161,
2166
] | Train |
2,168 | 6 | Title: Malicious Membership Queries and Exceptions
Abstract: Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, determination of liability, and policy analysis. We present a method for evaluating counter-factuals when the underlying causal model is represented by structural models a nonlinear generalization of the simultaneous equations models commonly used in econometrics and social sciences. This new method provides a coherent means for evaluating policies involving the control of variables which, prior to enacting the policy were influenced by other variables in the system. | [
1363,
2350
] | Train |
2,169 | 3 | Title: Theory Refinement on Bayesian Networks
Abstract: Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and able to be incrementally refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well in batch and incremental mode. | [
1290,
2420
] | Test |
2,170 | 1 | Title: Generalist and Specialist Behavior Due to Individual Energy Extracting Abilities.
Abstract: The emergence of generalist and specialist behavior in populations of neural networks is studied. Energy extracting ability is included as a property of an organism. In artificial life simulations with organisms living in an environment, the fitness score can be interpreted as the combination of an organisms behavior and the ability of the organism to extract energy from potential food sources distributed in the environment. The energy extracting ability is viewed as an evolvable trait of organisms a particular organism's mechanisms for extracting energy from the environment and, therefore, it is not fixed and decided by the researcher. Simulations with fixed and evolvable energy extracting abilities show that the energy extracting mechanism, the sensory apparatus, and the behavior of organisms may co-evolve and be co-adapted. The results suggest that populations of organisms evolve to be generalists or specialists due to individual energy extracting abilities. | [
1325,
2237
] | Train |
2,171 | 5 | Title: K unstliche Intelligenz Grdt: Enhancing Model-Based Learning for Its Application in Robot Navigation
Abstract: The emergence of generalist and specialist behavior in populations of neural networks is studied. Energy extracting ability is included as a property of an organism. In artificial life simulations with organisms living in an environment, the fitness score can be interpreted as the combination of an organisms behavior and the ability of the organism to extract energy from potential food sources distributed in the environment. The energy extracting ability is viewed as an evolvable trait of organisms a particular organism's mechanisms for extracting energy from the environment and, therefore, it is not fixed and decided by the researcher. Simulations with fixed and evolvable energy extracting abilities show that the energy extracting mechanism, the sensory apparatus, and the behavior of organisms may co-evolve and be co-adapted. The results suggest that populations of organisms evolve to be generalists or specialists due to individual energy extracting abilities. | [
344,
638,
2031,
2032
] | Test |
2,172 | 6 | Title: Tractability of Theory Patching
Abstract: In this paper we consider the problem of theory patching, in which we are given a domain theory, some of whose components are indicated to be possibly flawed, and a set of labeled training examples for the domain concept. The theory patching problem is to revise only the indicated components of the theory, such that the resulting theory correctly classifies all the training examples. Theory patching is thus a type of theory revision in which revisions are made to individual components of the theory. Our concern in this paper is to determine for which classes of logical domain theories the theory patching problem is tractable. We consider both propositional and first-order domain theories, and show that the theory patching problem is equivalent to that of determining what information contained in a theory is stable regardless of what revisions might be performed to the theory. We show that determining stability is tractable if the input theory satisfies two conditions: that revisions to each theory component have monotonic effects on the classification of examples, and that theory components act independently in the classification of examples in the theory. We also show how the concepts introduced can be used to determine the soundness and completeness of particular theory patching algorithms. | [
136,
159,
2692
] | Train |
2,173 | 1 | Title: Adapting Control Strategies for Situated Autonomous Agents
Abstract: This paper studies how to balance evolutionary design and human expertise in order to best design situated autonomous agents which can learn specific tasks. A genetic algorithm designs control circuits to learn simple behaviors, and given control strategies for simple behaviors, the genetic algorithm designs a combinational circuit that switches between these simple behaviors to perform a navigation task. Keywords: Genetic Algorithms, Computational Design, Autonomous Agents, Robotics. | [
163,
636,
846,
2204
] | Test |
2,174 | 4 | Title: The Role of the Trainer in Reinforcement Learning
Abstract: In this paper we propose a threestage incremental approach to the development of autonomous agents. We discuss some issues about the characteristics which differentiate reinforcement programs (RPs), and define the trainer as a particular kind of RP. We present a set of results obtained running experiments with a trainer which provides guidance to the AutonoMouse, our mousesized autonomous robot. | [
636,
764,
1573,
2687
] | Train |
2,175 | 1 | Title: The Troubling Aspects of a Building Block Hypothesis for Genetic Programming
Abstract: In this paper we carefully formulate a Schema Theorem for Genetic Programming (GP) using a schema definition that accounts for the variable length and the non-homologous nature of GP's representation. In a manner similar to early GA research, we use interpretations of our GP Schema Theorem to obtain a GP Building Block definition and to state a "classical" Building Block Hypothesis (BBH): that GP searches by hierarchically combining building blocks. We report that this approach is not convincing for several reasons: it is difficult to find support for the promotion and combination of building blocks solely by rigourous interpretation of a GP Schema Theorem; even if there were such support for a BBH, it is empirically questionable whether building blocks always exist because partial solutions of consistently above average fitness and resilience to disruption are not assured; also, a BBH constitutes a narrow and imprecise account of GP search behavior. | [
120,
163,
1009,
1257,
1362,
1696,
1719,
1745,
1940,
2087,
2206,
2249,
2250,
2259,
2361
] | Train |
2,176 | 2 | Title: An Analytical Framework for Local Feedforward Networks
Abstract: Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To understand these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multi-layer perceptron (MLP) networks that employ the back-propagation learning algorithm, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently non-local by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs can be made arbitrarily local while retaining the ability to approximate any continuous function on a compact domain. fl Partially supported under Task 2312 R1 by the United States Air Force Office of Scientific Research. | [
1914,
2535
] | Test |
2,177 | 1 | Title: Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal
Abstract: University of Wisconsin-Madison, Department of Computer Sciences Technical Report CS-TR-94-1259 Abstract The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) [46] is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structures emerge. In this report, we examine one particular IPD/CR environment and document the social network methods used to identify population behaviors found within this complex adaptive system. In contrast to the standard homogeneous population of nice cooperators, we have also found metastable populations of mixed strategies within this environment. In particular, the social networks of interesting populations and their evolution are examined. | [
163,
1883
] | Test |
2,178 | 2 | Title: Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading
Abstract: A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multi-variate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on out-of-sample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient. | [
1788,
1793,
1794,
1795,
2082,
2181,
2545,
2582
] | Train |
2,179 | 4 | Title: Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models
Abstract: The close connection between reinforcement learning (RL) algorithms and dynamic programming algorithms has fueled research on RL within the machine learning community. Yet, despite increased theoretical understanding, RL algorithms remain applicable to simple tasks only. In this paper I use the abstract framework afforded by the connection to dynamic programming to discuss the scaling issues faced by RL researchers. I focus on learning agents that have to learn to solve multiple structured RL tasks in the same environment. I propose learning abstract environment models where the abstract actions represent "intentions" of achieving a particular state. Such models are variable temporal resolution models because in different parts of the state space the abstract actions span different number of time steps. The operational definitions of abstract actions can be learned incrementally using repeated experience at solving RL tasks. I prove that under certain conditions solutions to new RL tasks can be found by using simu lated experience with abstract actions alone. | [
321,
2150,
2183
] | Validation |
2,180 | 6 | Title: Oblivious Decision Trees, Graphs, and Top-Down Pruning
Abstract: We describe a supervised learning algorithm, EODG, that uses mutual information to build an oblivious decision tree. The tree is then converted to an Oblivious read-Once Decision Graph (OODG) by merging nodes at the same level of the tree. For domains that are appropriate for both decision trees and OODGs, performance is approximately the same as that of C4.5, but the number of nodes in the OODG is much smaller. The merging phase that converts the oblivious decision tree to an OODG provides a new way of dealing with the replication problem and a new pruning mechanism that works top down starting from the root. The pruning mechanism is well suited for finding symmetries and aids in recovering from splits on irrelevant features that may happen during the tree construction. | [
1500,
2577
] | Validation |
2,181 | 2 | Title: Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG
Abstract: A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing large-scale properties of short-term memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. Sets of EEG and evoked potential data were fit, collected to investigate genetic predispositions to alcoholism and to extract brain signatures of short-term memory. Adaptive Simulated Annealing (ASA), a global optimization algorithm, was used to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta indicators (CMI) are thereby derived for individual's EEG data. The CMI give better signal recognition than the raw data, and can be used to advantage as correlates of behavioral states. These results give strong quantitative support for an accurate intuitive picture, portraying neocortical interactions as having common algebraic or physics mechanisms that scale across quite disparate spatial scales and functional or behavioral phenomena, i.e., describing interactions among neurons, columns of neurons, and regional masses of neurons. This paper adds to these previous investigations two important aspects, a description of how the CMI may be used in source localization, and calculations using previously ASA-fitted parameters in out-of-sample data. | [
1788,
1793,
1795,
2143,
2178
] | Validation |
2,182 | 6 | Title: Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis
Abstract: We present new results, both positive and negative, on the well-studied problem of learning disjunctive normal form (DNF) expressions. We first prove that an algorithm due to Kushilevitz and Mansour [16] can be used to weakly learn DNF using membership queries in polynomial time, with respect to the uniform distribution on the inputs. This is the first positive result for learning unrestricted DNF expressions in polynomial time in any nontrivial formal model of learning. It provides a sharp contrast with the results of Kharitonov [15], who proved that AC 0 is not efficiently learnable in the same model (given certain plausible cryptographic assumptions). We also present efficient learning algorithms in various models for the read-k and SAT-k subclasses of DNF. For our negative results, we turn our attention to the recently introduced statistical query model of learning [11]. This model is a restricted version of the popular Probably Approximately Correct (PAC) model [23], and practically every class known to be efficiently learnable in the PAC model is in fact learnable in the statistical query model [11]. Here we give a general characterization of the complexity of statistical query learning in terms of the number of uncorrelated functions in the concept class. This is a distribution-dependent quantity yielding upper and lower bounds on the number of statistical queries required for learning on any input distribution. As a corollary, we obtain that DNF expressions and decision trees are not even weakly learnable with fl This research is sponsored in part by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. Support also is sponsored by the National Science Foundation under Grant No. CC-9119319. Blum also supported in part by NSF National Young Investigator grant CCR-9357793. Views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of Wright Laboratory or the United States Government, or NSF. respect to the uniform input distribution in polynomial time in the statistical query model. This result is information-theoretic and therefore does not rely on any unproven assumptions. It demonstrates that no simple modification of the existing algorithms in the computational learning theory literature for learning various restricted forms of DNF and decision trees from passive random examples (and also several algorithms proposed in the experimental machine learning communities, such as the ID3 algorithm for decision trees [22] and its variants) will solve the general problem. The unifying tool for all of our results is the Fourier analysis of a finite class of boolean functions on the hypercube. | [
591,
1003,
1748,
1835,
1897,
2011,
2146,
2633
] | Test |
2,183 | 4 | Title: Multi-time Models for Temporally
Abstract: Planning Abstract Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an approach to this problem based on the mathematical framework of Markov decision processes and reinforcement learning. Current model-based reinforcement learning is based on one-step models that cannot represent common-sense higher-level actions, such as going to lunch, grasping an object, or flying to Denver. This paper generalizes prior work on temporally abstract models (Sutton, 1995b) and extends it from the prediction setting to include actions, control, and planning. We introduce a more general form of temporally abstract model, the multi-time model, and establish its suitability for planning and learning by virtue of its relationship to Bellman equations. This paper summarizes the theoretical framework of multi-time models and illustrates their potential ad The need for hierarchical and abstract planning is a fundamental problem in AI (see, e.g., Sacerdoti, 1977; Laird et al., 1986; Korf, 1985; Kaelbling, 1993; Dayan & Hinton, 1993). Model-based reinforcement learning offers a possible solution to the problem of integrating planning with real-time learning and decision-making (Peng & Williams, 1993, Moore & Atkeson, 1993; Sutton and Barto, in press). However, current model-based reinforcement learning is based on one-step models that cannot represent common-sense, higher-level actions. Modeling such actions requires the ability to handle different, interrelated levels of temporal abstraction. A new approach to modeling at multiple time scales was introduced by Sutton (1995b) based on prior work by Singh (1992), Dayan (1993b), and Sutton and Pinette (1985). This approach enables models of the environment at different temporal scales to be intermixed, producing temporally abstract models. However, that work was concerned only with predicting the environment. This paper summarizes vantages in a gridworld planning task. | [
1954,
2150,
2179,
2305
] | Train |
2,184 | 0 | Title: AN ARCHITECTURE FOR GOAL-DRIVEN EXPLANATION
Abstract: In complex and changing environments explanation must be a a dynamic and goal-driven process. This paper discusses an evolving system implementing a novel model of explanation generation | Goal-Driven Interactive Explanation | that models explanation as a goal-driven, multi-strategy, situated process inter-weaving reasoning with action. We describe a preliminary implementation of this model in gobie, a system that generates explanations for its internal use to support plan generation and execution. | [
2398
] | Validation |
2,185 | 2 | Title: of nucleotide sites needed to accurately reconstruct large evolutionary trees 1
Abstract: DIMACS Technical Report 96-19 July 1996 | [
2109
] | Test |
2,186 | 2 | Title: A REMARK ON ROBUST STABILIZATION OF GENERAL ASYMPTOTICALLY CONTROLLABLE SYSTEMS
Abstract: It was shown recently by Clarke, Ledyaev, Sontag and Subbotin that any asymptotically controllable system can be stabilized by means of a certain type of discontinuous feedback. The feedback laws constructed in that work are robust with respect to actuator errors as well as to perturbations of the system dynamics. A drawback, however, is that they may be highly sensitive to errors in the measurement of the state vector. This paper addresses this shortcoming, and shows how to design a dynamic hybrid stabilizing controller which, while preserving robustness to external perturbations and actuator error, is also robust with respect to measurement error. This new design relies upon a controller which incorporates an internal model of the system driven by the previously constructed feedback. | [
2321
] | Test |
2,187 | 2 | Title: "UNIVERSAL" CONSTRUCTION OF ARTSTEIN'S THEOREM ON NONLINEAR STABILIZATION 1
Abstract: Report SYCON-89-03 ABSTRACT This note presents an explicit proof of the theorem -due to Artstein- which states that the existence of a smooth control-Lyapunov function implies smooth stabilizability. More- over, the result is extended to the real-analytic and rational cases as well. The proof uses a "universal" formula given by an algebraic function of Lie derivatives; this formula originates in the solution of a simple Riccati equation. | [
531,
2314,
2321
] | Test |
2,188 | 5 | Title: Improving Software Pipelining With Unroll-and-Jam
Abstract: In this paper, we demonstrate how unroll-and-jam can significantly improve the initiation interval in a software-pipelined loop. Improvements in the initiation interval of greater than 40% are common, while dramatic improvements of a factor of 5 are possible. | [
2149,
2189,
2190,
2194
] | Validation |
2,189 | 5 | Title: Stage Scheduling: A Technique to Reduce the Register Requirements of a Modulo Schedule
Abstract: Modulo scheduling is an efficient technique for exploiting instruction level parallelism in a variety of loops, resulting in high performance code but increased register requirements. We present a set of low computational complexity stage-scheduling heuristics that reduce the register requirements of a given modulo schedule by shifting operations by multiples of II cycles. Measurements on a benchmark suite of 1289 loops from the Perfect Club, SPEC-89, and the Livermore Fortran Kernels shows that our best heuristic achieves on average 99% of the decrease in register requirements obtained by an optimal stage scheduler. | [
2188,
2190,
2194,
2365
] | Train |
2,190 | 5 | Title: Minimum Register Requirements for a Modulo Schedule
Abstract: Modulo scheduling is an efficient technique for exploiting instruction level parallelism in a variety of loops, resulting in high performance code but increased register requirements. We present a combined approach that schedules the loop operations for minimum register requirements, given a modulo reservation table. Our method determines optimal register requirements for machines with finite resources and for general dependence graphs. This method demonstrates the potential of lifetime-sensitive modulo scheduling and is useful in evaluating the performance of lifetime-sensitive modulo scheduling heuristics. | [
1955,
2149,
2188,
2189,
2194,
2365
] | Validation |
2,191 | 2 | Reference: [Tex89] <institution> Texas Instruments. </institution> <note> TMS320C30 C Compiler Reference Guide, 1989. Document Title: SPRU034A. </note>
Abstract: The design and implementation of software for the Ring Array Processor (RAP), a high performance parallel computer, involved development for three hardware platforms: Sun SPARC workstations, Heurikon MC68020 boards running the VxWorks real-time operating system, and Texas Instruments TMS320C30 DSPs. The RAP now runs in Sun workstations under UNIX and in a VME based system using VxWorks. A flexible set of tools has been provided both to the RAP user and programmer. Primary emphasis has been placed on improving the efficiency of layered artificial neural network algorithms. This was done by providing a library of assembly language routines, some of which use node-custom compilation. An object-oriented RAP interface in C++ is provided that allows programmers to incorporate the RAP as a computational server into their own UNIX applications. For those not wishing to program in C++, a command interpreter has been built that provides interactive and shell-script style RAP manipulation. | [
362,
2275
] | Validation |
2,192 | 1 | Title: #1 Robust Feature Selection Algorithms
Abstract: Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Our experience with traditional feature selection algorithms in the domain of machine learning lead to an appreciation for their computational efficiency and a concern for their brittleness. This paper describes an alternate approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggest that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in computational efficiency. | [
177,
1743
] | Train |
2,193 | 1 | Title: Growing neural networks
Abstract: Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Our experience with traditional feature selection algorithms in the domain of machine learning lead to an appreciation for their computational efficiency and a concern for their brittleness. This paper describes an alternate approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggest that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in computational efficiency. | [
129,
538,
2165
] | Train |
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