58 research outputs found
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities
Multimodal datasets contain an enormous amount of relational information,
which grows exponentially with the introduction of new modalities. Learning
representations in such a scenario is inherently complex due to the presence of
multiple heterogeneous information channels. These channels can encode both (a)
inter-relations between the items of different modalities and (b)
intra-relations between the items of the same modality. Encoding multimedia
items into a continuous low-dimensional semantic space such that both types of
relations are captured and preserved is extremely challenging, especially if
the goal is a unified end-to-end learning framework. The two key challenges
that need to be addressed are: 1) the framework must be able to merge complex
intra and inter relations without losing any valuable information and 2) the
learning model should be invariant to the addition of new and potentially very
different modalities. In this paper, we propose a flexible framework which can
scale to data streams from many modalities. To that end we introduce a
hypergraph-based model for data representation and deploy Graph Convolutional
Networks to fuse relational information within and across modalities. Our
approach provides an efficient solution for distributing otherwise extremely
computationally expensive or even unfeasible training processes across
multiple-GPUs, without any sacrifices in accuracy. Moreover, adding new
modalities to our model requires only an additional GPU unit keeping the
computational time unchanged, which brings representation learning to truly
multimodal datasets. We demonstrate the feasibility of our approach in the
experiments on multimedia datasets featuring second, third and fourth order
relations
Hypergraph Motifs and Their Extensions Beyond Binary
Hypergraphs naturally represent group interactions, which are omnipresent in
many domains: collaborations of researchers, co-purchases of items, and joint
interactions of proteins, to name a few. In this work, we propose tools for
answering the following questions: (Q1) what are the structural design
principles of real-world hypergraphs? (Q2) how can we compare local structures
of hypergraphs of different sizes? (Q3) how can we identify domains from which
hypergraphs are? We first define hypergraph motifs (h-motifs), which describe
the overlapping patterns of three connected hyperedges. Then, we define the
significance of each h-motif in a hypergraph as its occurrences relative to
those in properly randomized hypergraphs. Lastly, we define the characteristic
profile (CP) as the vector of the normalized significance of every h-motif.
Regarding Q1, we find that h-motifs' occurrences in 11 real-world hypergraphs
from 5 domains are clearly distinguished from those of randomized hypergraphs.
Then, we demonstrate that CPs capture local structural patterns unique to each
domain, and thus comparing CPs of hypergraphs addresses Q2 and Q3. The concept
of CP is extended to represent the connectivity pattern of each node or
hyperedge as a vector, which proves useful in node classification and hyperedge
prediction. Our algorithmic contribution is to propose MoCHy, a family of
parallel algorithms for counting h-motifs' occurrences in a hypergraph. We
theoretically analyze their speed and accuracy and show empirically that the
advanced approximate version MoCHy-A+ is more accurate and faster than the
basic approximate and exact versions, respectively. Furthermore, we explore
ternary hypergraph motifs that extends h-motifs by taking into account not only
the presence but also the cardinality of intersections among hyperedges. This
extension proves beneficial for all previously mentioned applications.Comment: Extended version of VLDB 2020 paper arXiv:2003.0185
Structural and Dynamical Properties of Complex Networks.
Recent years have witnessed a substantial amount of interest within the physics community
in the properties of networks. Techniques from statistical physics coupled with the widespread availability of computing resources have facilitated studies ranging from large scale empirical analysis of the worldwide web, social networks, biological systems, to the development of theoretical models and tools to explore the various properties of these systems.
Following these developments, in this
dissertation, we present and solve for a diverse
set of new problems, investigating the structural and dynamical properties of both model and real world networks.We start by defining a new metric to measure the stability of network structure to
disruptions, and then using a combination of theory and simulation study its properties
in detail on artificially generated networks; we then compare our results to a selection
of networks from the real world and find good agreement in most cases. In the following
chapter, we propose a mathematical model that mimics the structure of popular file-sharing websites such as Flickr and CiteULike and demonstrate that many of its properties can solved exactly in the limit of large network size. The remaining part of the dissertation primarily focuses on the dynamical properties of networks. We first formulate a model of a network that evolves under the addition and deletion of vertices and edges, and solve for the equilibrium degree distribution for a variety of cases of interest. We then consider networks whose structure can be manipulated by adjusting the rules by which vertices enter and leave the network. We focus in particular on degree
distributions and show that, with some mild constraints, it is possible by a suitable choice of rules to arrange for the network to have any degree distribution we desire. In addition we define a simple local algorithm by which appropriate rules can be implemented in practice. Finally, we conclude our dissertation with a game theory model on social networks that tracks the dynamical evolution of a group of interacting agents such as diplomats or political lobbyists seeking to rise to a position of influence, by balancing competing interests.Ph.D.PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64757/1/gghoshal_1.pd
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
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