3,197 research outputs found
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
The study and understanding of human behaviour is relevant to computer
science, artificial intelligence, neural computation, cognitive science,
philosophy, psychology, and several other areas. Presupposing cognition as
basis of behaviour, among the most prominent tools in the modelling of
behaviour are computational-logic systems, connectionist models of cognition,
and models of uncertainty. Recent studies in cognitive science, artificial
intelligence, and psychology have produced a number of cognitive models of
reasoning, learning, and language that are underpinned by computation. In
addition, efforts in computer science research have led to the development of
cognitive computational systems integrating machine learning and automated
reasoning. Such systems have shown promise in a range of applications,
including computational biology, fault diagnosis, training and assessment in
simulators, and software verification. This joint survey reviews the personal
ideas and views of several researchers on neural-symbolic learning and
reasoning. The article is organised in three parts: Firstly, we frame the scope
and goals of neural-symbolic computation and have a look at the theoretical
foundations. We then proceed to describe the realisations of neural-symbolic
computation, systems, and applications. Finally we present the challenges
facing the area and avenues for further research.Comment: 58 pages, work in progres
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
A fundamental challenge in developing high-impact machine learning
technologies is balancing the need to model rich, structured domains with the
ability to scale to big data. Many important problem areas are both richly
structured and large scale, from social and biological networks, to knowledge
graphs and the Web, to images, video, and natural language. In this paper, we
introduce two new formalisms for modeling structured data, and show that they
can both capture rich structure and scale to big data. The first, hinge-loss
Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model
that generalizes different approaches to convex inference. We unite three
approaches from the randomized algorithms, probabilistic graphical models, and
fuzzy logic communities, showing that all three lead to the same inference
objective. We then define HL-MRFs by generalizing this unified objective. The
second new formalism, probabilistic soft logic (PSL), is a probabilistic
programming language that makes HL-MRFs easy to define using a syntax based on
first-order logic. We introduce an algorithm for inferring most-probable
variable assignments (MAP inference) that is much more scalable than
general-purpose convex optimization methods, because it uses message passing to
take advantage of sparse dependency structures. We then show how to learn the
parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous
discrete models, but much more scalable. Together, these algorithms enable
HL-MRFs and PSL to model rich, structured data at scales not previously
possible
Structural Damage Identification Using Artificial Neural Network and Synthetic data
This paper presents real-time vibration based identification technique using
measured frequency response functions(FRFs) under random vibration loading.
Artificial Neural Networks (ANNs) are trained to map damage fingerprints to
damage characteristic parameters. Principal component statistical analysis(PCA)
technique was used to tackle the problem of high dimensionality and high noise
of data, which is common for industrial structures. The present study considers
Crack, Rivet hole expansion and redundant uniform mass as damages on the
structure. Frequency response function data after being reduced in size using
PCA is fed to individual neural networks to localize and predict the severity
of damage on the structure. The system of ANNs trained with both numerical and
experimental model data to make the system reliable and robust. The methodology
is applied to a numerical model of stiffened panel structure, where damages are
confined close to the stiffener. The results showed that, in all the cases
considered, it is possible to localize and predict severity of the damage
occurrence with very good accuracy and reliability.Comment: 6 pages,6 figures, ISSS conferenc
TensorLog: A Differentiable Deductive Database
Large knowledge bases (KBs) are useful in many tasks, but it is unclear how
to integrate this sort of knowledge into "deep" gradient-based learning
systems. To address this problem, we describe a probabilistic deductive
database, called TensorLog, in which reasoning uses a differentiable process.
In TensorLog, each clause in a logical theory is first converted into certain
type of factor graph. Then, for each type of query to the factor graph, the
message-passing steps required to perform belief propagation (BP) are
"unrolled" into a function, which is differentiable. We show that these
functions can be composed recursively to perform inference in non-trivial
logical theories containing multiple interrelated clauses and predicates. Both
compilation and inference in TensorLog are efficient: compilation is linear in
theory size and proof depth, and inference is linear in database size and the
number of message-passing steps used in BP. We also present experimental
results with TensorLog and discuss its relationship to other first-order
probabilistic logics
Treatment of Semantic Heterogeneity in Information Retrieval
The first step to handle semantic heterogeneity should be the attempt to
enrich the semantic information about documents, i.e. to fill up the gaps in
the documents meta-data automatically. Section 2 describes a set of cascading
deductive and heuristic extraction rules, which were developed in the project
CARMEN for the domain of Social Sciences. The mapping between different
terminologies can be done by using intellectual, statistical and/or neural
network transfer modules. Intellectual transfers use cross-concordances between
different classification schemes or thesauri. Section 3 describes the creation,
storage and handling of such transfers.Comment: Technical Report (Arbeitsbericht) GESIS - Leibniz Institute for the
Social Science
Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks
Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin
Incremental Knowledge Base Construction Using DeepDive
Populating a database with unstructured information is a long-standing
problem in industry and research that encompasses problems of extraction,
cleaning, and integration. Recent names used for this problem include dealing
with dark data and knowledge base construction (KBC). In this work, we describe
DeepDive, a system that combines database and machine learning ideas to help
develop KBC systems, and we present techniques to make the KBC process more
efficient. We observe that the KBC process is iterative, and we develop
techniques to incrementally produce inference results for KBC systems. We
propose two methods for incremental inference, based respectively on sampling
and variational techniques. We also study the tradeoff space of these methods
and develop a simple rule-based optimizer. DeepDive includes all of these
contributions, and we evaluate DeepDive on five KBC systems, showing that it
can speed up KBC inference tasks by up to two orders of magnitude with
negligible impact on quality
Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries
The complexity of the visual world creates significant challenges for
comprehensive visual understanding. In spite of recent successes in visual
recognition, today's vision systems would still struggle to deal with visual
queries that require a deeper reasoning. We propose a knowledge base (KB)
framework to handle an assortment of visual queries, without the need to train
new classifiers for new tasks. Building such a large-scale multimodal KB
presents a major challenge of scalability. We cast a large-scale MRF into a KB
representation, incorporating visual, textual and structured data, as well as
their diverse relations. We introduce a scalable knowledge base construction
system that is capable of building a KB with half billion variables and
millions of parameters in a few hours. Our system achieves competitive results
compared to purpose-built models on standard recognition and retrieval tasks,
while exhibiting greater flexibility in answering richer visual queries
Exploring Connections Between Active Learning and Model Extraction
Machine learning is being increasingly used by individuals, research
institutions, and corporations. This has resulted in the surge of Machine
Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and
resources to learn the model, and (b) a user-friendly query interface to access
the model. However, such MLaaS systems raise privacy concerns such as model
extraction. In model extraction attacks, adversaries maliciously exploit the
query interface to steal the model. More precisely, in a model extraction
attack, a good approximation of a sensitive or proprietary model held by the
server is extracted (i.e. learned) by a dishonest user who interacts with the
server only via the query interface. This attack was introduced by Tramer et
al. at the 2016 USENIX Security Symposium, where practical attacks for various
models were shown. We believe that better understanding the efficacy of model
extraction attacks is paramount to designing secure MLaaS systems. To that end,
we take the first step by (a) formalizing model extraction and discussing
possible defense strategies, and (b) drawing parallels between model extraction
and established area of active learning. In particular, we show that recent
advancements in the active learning domain can be used to implement powerful
model extraction attacks, and investigate possible defense strategies
Components of Soft Computing for Epileptic Seizure Prediction and Detection
Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection
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