7,448 research outputs found
Exploring the structure of a real-time, arbitrary neural artistic stylization network
In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference
(BMVC) 201
Data exploration systems for databases
Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Given the unprecedented availability of data and computing resources, there
is widespread renewed interest in applying data-driven machine learning methods
to problems for which the development of conventional engineering solutions is
challenged by modelling or algorithmic deficiencies. This tutorial-style paper
starts by addressing the questions of why and when such techniques can be
useful. It then provides a high-level introduction to the basics of supervised
and unsupervised learning. For both supervised and unsupervised learning,
exemplifying applications to communication networks are discussed by
distinguishing tasks carried out at the edge and at the cloud segments of the
network at different layers of the protocol stack
Modeling Relational Data via Latent Factor Blockmodel
In this paper we address the problem of modeling relational data, which
appear in many applications such as social network analysis, recommender
systems and bioinformatics. Previous studies either consider latent feature
based models but disregarding local structure in the network, or focus
exclusively on capturing local structure of objects based on latent blockmodels
without coupling with latent characteristics of objects. To combine the
benefits of the previous work, we propose a novel model that can simultaneously
incorporate the effect of latent features and covariates if any, as well as the
effect of latent structure that may exist in the data. To achieve this, we
model the relation graph as a function of both latent feature factors and
latent cluster memberships of objects to collectively discover globally
predictive intrinsic properties of objects and capture latent block structure
in the network to improve prediction performance. We also develop an
optimization transfer algorithm based on the generalized EM-style strategy to
learn the latent factors. We prove the efficacy of our proposed model through
the link prediction task and cluster analysis task, and extensive experiments
on the synthetic data and several real world datasets suggest that our proposed
LFBM model outperforms the other state of the art approaches in the evaluated
tasks.Comment: 10 pages, 12 figure
Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Matrix factorization techniques have been widely used as a method for
collaborative filtering for recommender systems. In recent times, different
variants of deep learning algorithms have been explored in this setting to
improve the task of making a personalized recommendation with user-item
interaction data. The idea that the mapping between the latent user or item
factors and the original features is highly nonlinear suggest that classical
matrix factorization techniques are no longer sufficient. In this paper, we
propose a multilayer nonlinear semi-nonnegative matrix factorization method,
with the motivation that user-item interactions can be modeled more accurately
using a linear combination of non-linear item features. Firstly, we learn
latent factors for representations of users and items from the designed
multilayer nonlinear Semi-NMF approach using explicit ratings. Secondly, the
architecture built is compared with deep-learning algorithms like Restricted
Boltzmann Machine and state-of-the-art Deep Matrix factorization techniques. By
using both supervised rate prediction task and unsupervised clustering in
latent item space, we demonstrate that our proposed approach achieves better
generalization ability in prediction as well as comparable representation
ability as deep matrix factorization in the clustering task.Comment: version
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Modern convolutional neural networks (CNNs) are able to achieve human-level
object classification accuracy on specific tasks, and currently outperform
competing models in explaining complex human visual representations. However,
the categorization problem is posed differently for these networks than for
humans: the accuracy of these networks is evaluated by their ability to
identify single labels assigned to each image. These labels often cut
arbitrarily across natural psychological taxonomies (e.g., dogs are separated
into breeds, but never jointly categorized as "dogs"), and bias the resulting
representations. By contrast, it is common for children to hear both "dog" and
"Dalmatian" to describe the same stimulus, helping to group perceptually
disparate objects (e.g., breeds) into a common mental class. In this work, we
train CNN classifiers with multiple labels for each image that correspond to
different levels of abstraction, and use this framework to reproduce classic
patterns that appear in human generalization behavior.Comment: 6 pages, 4 figures, 1 table. Accepted as a paper to the 40th Annual
Meeting of the Cognitive Science Society (CogSci 2018
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Conventional methods of estimating latent behaviour generally use attitudinal
questions which are subjective and these survey questions may not always be
available. We hypothesize that an alternative approach can be used for latent
variable estimation through an undirected graphical models. For instance,
non-parametric artificial neural networks. In this study, we explore the use of
generative non-parametric modelling methods to estimate latent variables from
prior choice distribution without the conventional use of measurement
indicators. A restricted Boltzmann machine is used to represent latent
behaviour factors by analyzing the relationship information between the
observed choices and explanatory variables. The algorithm is adapted for latent
behaviour analysis in discrete choice scenario and we use a graphical approach
to evaluate and understand the semantic meaning from estimated parameter vector
values. We illustrate our methodology on a financial instrument choice dataset
and perform statistical analysis on parameter sensitivity and stability. Our
findings show that through non-parametric statistical tests, we can extract
useful latent information on the behaviour of latent constructs through machine
learning methods and present strong and significant influence on the choice
process. Furthermore, our modelling framework shows robustness in input
variability through sampling and validation
A Model of Consistent Node Types in Signed Directed Social Networks
Signed directed social networks, in which the relationships between users can
be either positive (indicating relations such as trust) or negative (indicating
relations such as distrust), are increasingly common. Thus the interplay
between positive and negative relationships in such networks has become an
important research topic. Most recent investigations focus upon edge sign
inference using structural balance theory or social status theory. Neither of
these two theories, however, can explain an observed edge sign well when the
two nodes connected by this edge do not share a common neighbor (e.g., common
friend). In this paper we develop a novel approach to handle this situation by
applying a new model for node types. Initially, we analyze the local node
structure in a fully observed signed directed network, inferring underlying
node types. The sign of an edge between two nodes must be consistent with their
types; this explains edge signs well even when there are no common neighbors.
We show, moreover, that our approach can be extended to incorporate directed
triads, when they exist, just as in models based upon structural balance or
social status theory. We compute Bayesian node types within empirical studies
based upon partially observed Wikipedia, Slashdot, and Epinions networks in
which the largest network (Epinions) has 119K nodes and 841K edges. Our
approach yields better performance than state-of-the-art approaches for these
three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in
Social Network Analysis and Mining (ASONAM), 201
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