46,694 research outputs found
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
Fault Detection Effectiveness of Metamorphic Relations Developed for Testing Supervised Classifiers
In machine learning, supervised classifiers are used to obtain predictions
for unlabeled data by inferring prediction functions using labeled data.
Supervised classifiers are widely applied in domains such as computational
biology, computational physics and healthcare to make critical decisions.
However, it is often hard to test supervised classifiers since the expected
answers are unknown. This is commonly known as the \emph{oracle problem} and
metamorphic testing (MT) has been used to test such programs. In MT,
metamorphic relations (MRs) are developed from intrinsic characteristics of the
software under test (SUT). These MRs are used to generate test data and to
verify the correctness of the test results without the presence of a test
oracle. Effectiveness of MT heavily depends on the MRs used for testing. In
this paper we have conducted an extensive empirical study to evaluate the fault
detection effectiveness of MRs that have been used in multiple previous studies
to test supervised classifiers. Our study uses a total of 709 reachable mutants
generated by multiple mutation engines and uses data sets with varying
characteristics to test the SUT. Our results reveal that only 14.8\% of these
mutants are detected using the MRs and that the fault detection effectiveness
of these MRs do not scale with the increased number of mutants when compared to
what was reported in previous studies.Comment: 8 pages, AITesting 201
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology
available to every developer. In the near future, every application on every
platform will incorporate trained models to encode data-based decisions that
would be impossible for developers to author. This presents a significant
engineering challenge, since currently data science and modeling are largely
decoupled from standard software development processes. This separation makes
incorporating machine learning capabilities inside applications unnecessarily
costly and difficult, and furthermore discourage developers from embracing ML
in first place. In this paper we present ML .NET, a framework developed at
Microsoft over the last decade in response to the challenge of making it easy
to ship machine learning models in large software applications. We present its
architecture, and illuminate the application demands that shaped it.
Specifically, we introduce DataView, the core data abstraction of ML .NET which
allows it to capture full predictive pipelines efficiently and consistently
across training and inference lifecycles. We close the paper with a
surprisingly favorable performance study of ML .NET compared to more recent
entrants, and a discussion of some lessons learned
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
A Configurable Matchmaking Framework for Electronic Marketplaces
E-marketplaces constitute a major enabler of B2B and B2C e-commerce activities. This paper proposes a framework for one of the central activities of e-marketplaces: matchmaking of trading intentions lodged by market participants. The framework identifies a core set of concepts and functions that are common to all types of marketplaces and can serve as the basis for describing the distinct styles of matchmaking employed within various market mechanisms. A prototype implementation of the framework based on Web services technology is presented, illustrating its ability to be dynamically configured to meet specific market needs and its potential to serve as a foundation for more fully fledged e-marketplace frameworks
Quo vadimus? The 21st Century and multimedia
The concept is related of computer driven multimedia to the NASA Scientific and Technical Information Program (STIP). Multimedia is defined here as computer integration and output of text, animation, audio, video, and graphics. Multimedia is the stage of computer based information that allows access to experience. The concepts are also drawn in of hypermedia, intermedia, interactive multimedia, hypertext, imaging, cyberspace, and virtual reality. Examples of these technology developments are given for NASA, private industry, and academia. Examples of concurrent technology developments and implementations are given to show how these technologies, along with multimedia, have put us at the threshold of the 21st century. The STI Program sees multimedia as an opportunity for revolutionizing the way STI is managed
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