Algorithmic differentiation (AD) is a widely accepted methodology to obtain derivatives of scientific code for use in optimization, integration methods, and sensitivity analysis. Currently about 60 AD tools are listed on autodiff. org to compute sensitivity information. Several years have been invested in the research and development of popular implementations such as ADOL-C, CppAD, OpenAD, and...

Implemented methods for proper names recognition rely on large gazetteers of common proper nouns and a set of heuristic rules (e.g. Mr. as an indicator of a PERSON entity type). Though the performance of current PN recognizers is very high (over 90%), it is important to note that this problem is by no means a "solved problem". Existing systems perform extremely well on newswire corpora by virtu...

The data-flow of a numerical program is reversed in its adjoint. We discuss the combinatorial optimization problem that aims to find optimal checkpointing schemes at the level of call trees. For a given amount of persistent memory the objective is to store selected arguments and/or results of subroutine calls such that the overall computational effort (the total number of floating-point operati...

Many problems in machine learning involve objective functions that are piecewise smooth [7] due to the occurrence of absolute values mins and maxes in their evaluation procedures. See e.g. [8]. For such function we derived in [3] first order (KKT) and second order (SSC) optimality conditions, which can be checked on the basis of a local piecewise linearization [2] that can be computed in an AD ...

As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important. The implementation of the derivatives that make these algorithms so powerful, however, is a substantial user burden and the practicality of these algorithms depends critically on tools like automatic differentiation that remove the...

Children with pervasive developmental disorders (PDD) are impaired by early onset difficulties in reciprocal social interaction. It has been proposed that this social impairment is the result of a lack of Theory of Mind (ToM): the ability to attribute mental states (e.g., beliefs, desires, intentions, and emotions) to others and to use these in predicting and explaining the behavior of others (...

No AccessTechnical NotesPitfalls of Discrete Adjoint Fixed-Points Based on Algorithmic DifferentiationPedro Gomes and Rafael PalaciosPedro GomesImperial College, London, England SW7 2AZ, United Kingdom*Ph.D. Student, CAGB 308, Department Aeronautics, South Kensington Campus.Search for more papers by this author Palacios https://orcid.org/0000-0002-6706-3220Imperial Kingdom†Professor Computation...

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