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A parallel intermediate representation based on Lambda expressions
Full text linkThe lambda calculus has frequently been used as an intermediate representation for programming languages, particularly for functional programming language systems. We introduce two simple extensions to the lambda calculus that describe potentially parallel computations. These extensions permit us to use the lambda calculus as an intermediate form for languages that operate on large data items as single entities, such as FP or APL. We conclude by discussing how this intermediate representation can facilitate the generation of code for different types of parallel systems
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Learning and reasoning
Full text linkWhat is the relationship between learning and reasoning? Much recent work in machine learning has been criticized for focusing on learning and ignoring reasoning. This paper attempts to describe the various ways in which machine learning research has (and has not) incorporated reasoning. The paper argues that there are important computational, statistical, and engineering constraints that have produced the current state of affairs. These reasons are reviewed and assessed in the light of future research directions
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Learning diagnostic policies from examples by systematic search
No full textA diagnostic policy species what test to perform next based on the results of previous tests and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process MDP. We investigate two types of algorithms for solving this MDP systematic search based on the AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed that control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions it discusses the factors that make systematic search feasible for diagnosis and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.Keywords: Cost-sensitive diagnostic policies, greedy search, AO*, Markov Decision Proces
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LEDA : a blending of imperative and relational programming
Full text linkThis paper describes features of a new strongly typed, compiled programming language, called LEDA. LEDA attempts to combine aspects of both imperative (value-oriented) and logical (relation-oriented) styles of programming. Logical behavior is introduced by means of a new programming structure, called a relation. Relations have similarities to functions and procedures, but are distinct from both. Several examples are presented illustrating how the combination of features from the two paradigms are mutually beneficial. Finally a short overview of the implementation is given
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Multiparadigm data structures in Leda
Full text linkMultiparadigm programming is a term used to describe a style of software development that makes use of facilities originally designed in support of a number of different programming language paradigms. In this paper we illustrate our conception of multiparadigm programming, by describing how various data structures can be implemented in the programming language Leda. Leda is a strongly-typed compiled multiparadigm programming language that we have been developing over the past several years. Our exposition serves both to illustrate the idea of multiparadigm programming, and to describe the features of the language Leda
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Approximation algorithms for solving cost observable Markov decision processes
Full text link"The specifi c problem addressed in this proposal is the development of
good approximation algorithms for solving problems that have partial observability. The model we propose associates costs with obtaining information about the current state. We want to predict when and how much it is necessary to observe. We want to use our Cost Observable Markov Decision Process (COMDP) model to find good solutions for real-world problems ..."--Problem definition
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Generalized arithmetic in C++
Full text linkA generalized arithmetic package allows a programmer to think of numbers as abstract quantities, without regard to whether they are represented as integers, floating point values, fractions, complex numbers, or even polynomials. In this paper we describe two implementation techniques that can be used to produce a generalized arithmetic package in the programming language C++, and evaluate the advantages and disadvantages of the two approaches. The second approach uses a technique called multiple polymorphism that has applicability to many other problem areas.Keywords: C++, Polymorphism, Arithmetic, Object Oriented ProgrammingKeywords: C++, Polymorphism, Arithmetic, Object Oriented Programmin
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A new approach to vector code generation for applicative languages
Full text linkIn an earlier paper we developed an intermediate representation for languages based on composition, and showed how the representation could facilitate generating code for functional languages, such as FP. In this paper we follow the same philosophical approach, using instead the applicative language APL. Further, we show how this intermediate representation simplifies the task of generating code for highly parallel machines, such as the Connection Machine.Keywords: code generation, vector machines, applicative and functional language
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NET PROPHET : McCulloch and developments from his neural net model
Full text link"Fifty years after the pioneering work of McCulloch and Pitts, the study of neural nets is alive and active. In this paper, I have discussed some of the work that is of current interest to me and my co-workers. I would, perhaps, be remiss if I failed to mention some of the current hype about neural nets. Can neural nets quickly solve NP-complete problems? No. A look at the proposed nets will show that the question of whether the net will converge, or where the net will converge to, are as difficult as the original NP-complete problem. This does not prevent the neural net from giving an approximate solution to a hard optimization problem, but no one has yet proven any approximation bounds. Hard problems are only hard in the worst case, so there may be many easy instances of a hard problem. Nothing prevents a neural net from solving these easy instances quickly. Can analog neural nets compute things not computable a Turing machine? Yes. But any analog device with infinite precision has more computational power than a Turing machine, so a neural net with unlimited precision should be a very powerful device. But practically all devices are constructed with limited precision, and these limited precision devices have no more power than a Turing machine. Can neural nets compute faster than other parallel models? No. Neural nets are in fact equivalent to the usual parallel models. The only difference that can occur is if the neural net has infinite precision which as mentioned above is highly unlikely. Does learning in neural nets make programming unnecessary? No. As we saw in the discussion of learning, learning rules must be devised, and it seems that different learning tasks will require different learning rules. Further, the kind of net to use for a particular task will be an important decision. In our decoding example, some network topologies did not lead to good decoders, while other topologies did. Neural nets will not replace programmers, but give programmers another paradigm in which to program. In spite of the hype, I believe that neural nets will be useful both as biological models and as programming paradigms. Finally, according to an often-told tale, there was a golden age of neural nets which suddenly ended in 1970. Depending on the version of the tale, the golden age ended because of the Vietnam war, or Minsky and Papert's book on perceptions, or cuts in funding, or the rise of artificial intelligence. But I hope that the reader of this paper and the rest of this volume will see that the death of Warren McCulloch had a most profound effect on the field. We miss him as a brilliant scientist, as a warm human being, and as the greatest story-teller of our age."--Conclusion
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Parallel I/O requirements of four oceanography applications
Full text linkBrief descriptions of the I/O requirements for four production oceanography programs running at Oregon State University are presented. The applications all rely exclusively on array-oriented, sequential file operations. Persistent files are used for checkpointing and movie making, while temporary files are used to store out-of-core data
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