297 research outputs found
Scalable Text and Link Analysis with Mixed-Topic Link Models
Many data sets contain rich information about objects, as well as pairwise
relations between them. For instance, in networks of websites, scientific
papers, and other documents, each node has content consisting of a collection
of words, as well as hyperlinks or citations to other nodes. In order to
perform inference on such data sets, and make predictions and recommendations,
it is useful to have models that are able to capture the processes which
generate the text at each node and the links between them. In this paper, we
combine classic ideas in topic modeling with a variant of the mixed-membership
block model recently developed in the statistical physics community. The
resulting model has the advantage that its parameters, including the mixture of
topics of each document and the resulting overlapping communities, can be
inferred with a simple and scalable expectation-maximization algorithm. We test
our model on three data sets, performing unsupervised topic classification and
link prediction. For both tasks, our model outperforms several existing
state-of-the-art methods, achieving higher accuracy with significantly less
computation, analyzing a data set with 1.3 million words and 44 thousand links
in a few minutes.Comment: 11 pages, 4 figure
Existence and approximation of Hunt processes associated with generalized Dirichlet forms
We show that any strictly quasi-regular generalized Dirichlet form that
satisfies the mild structural condition D3 is associated to a Hunt process, and
that the associated Hunt process can be approximated by a sequence of
multivariate Poisson processes. This also gives a new proof for the existence
of a Hunt process associated to a strictly quasi-regular generalized Dirichlet
form that satisfies SD3 and extends all previous results.Comment: Revised, shortened and improved versio
Link Prediction Based on Local Random Walk
The problem of missing link prediction in complex networks has attracted much
attention recently. Two difficulties in link prediction are the sparsity and
huge size of the target networks. Therefore, the design of an efficient and
effective method is of both theoretical interests and practical significance.
In this Letter, we proposed a method based on local random walk, which can give
competitively good prediction or even better prediction than other
random-walk-based methods while has a lower computational complexity.Comment: 6 pages, 2 figure
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
Probabilistic Recognition of Complex Event
International audienceAbstract. This paper describes a complex event recognition approach with probabilistic reasoning for handling uncertainty. The first advantage of the proposed approach is the flexibility of the modeling of composite events with complex temporal constraints. The second advantage is the use of probability theory providing a consistent framework for dealing with uncertain knowledge for the recognition of complex events. The experimental results show that our system can successfully improve the event recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of the real-time event recognition
Statistical relational learning with soft quantifiers
Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results
Argument mining: A machine learning perspective
Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems
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