pwc-archive/papers-with-abstracts huggingface dataset:
An Online Hierarchical Algorithm for Extreme Clustering
Authors: Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum
Date: 2017-04-06 00:00:00
Tasks: Clustering

Many modern clustering methods scale well to a large number of data items, N,
but not to a large number of clusters, K. This paper introduces PERCH, a new
non-greedy algorithm for online hierarchical clustering that scales to both
massive N and K--a problem setting we term extreme clustering. Our algorithm
efficiently routes new data points to the leaves of an incrementally-built
tree. Motivated by the desire for both accuracy and speed, our approach
performs tree rotations for the sake of enhancing subtree purity and
encouraging balancedness. We prove that, under a natural separability
assumption, our non-greedy algorithm will produce trees with perfect dendrogram
purity regardless of online data arrival order. Our experiments demonstrate
that PERCH constructs more accurate trees than other tree-building clustering
algorithms and scales well with both N and K, achieving a higher quality
clustering than the strongest flat clustering competitor in nearly half the
time.
A Service-Oriented Architecture for Assisting the Authoring of Semantic Crowd Maps
Authors: Henrique Santos, Vasco Furtado
Date: 2017-04-06 00:00:00
Tasks: Common Sense Reasoning

Although there are increasingly more initiatives for the generation of
semantic knowledge based on user participation, there is still a shortage of
platforms for regular users to create applications on which semantic data can
be exploited and generated automatically. We propose an architecture, called
Semantic Maps (SeMaps), for assisting the authoring and hosting of applications
in which the maps combine the aggregation of a Geographic Information System
and crowd-generated content (called here crowd maps). In these systems, the
digital map works as a blackboard for accommodating stories told by people
about events they want to share with others typically participating in their
social networks. SeMaps offers an environment for the creation and maintenance
of sites based on crowd maps with the possibility for the user to characterize
semantically that which s/he intends to mark on the map. The designer of a
crowd map, by informing a linguistic expression that designates what has to be
marked on the maps, is guided in a process that aims to associate a concept
from a common-sense base to this linguistic expression. Thus, the crowd maps
start to have dominion over common-sense inferential relations that define the
meaning of the marker, and are able to make inferences about the network of
linked data. This makes it possible to generate maps that have the power to
perform inferences and access external sources (such as DBpedia) that
constitute information that is useful and appropriate to the context of the
map. In this paper we describe the architecture of SeMaps and how it was
applied in a crowd map authoring tool.
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification
Authors: Terence Fusco, Yaxin Bi, Haiying Wang, Fiona Browne
Date: 2017-04-06 00:00:00
Tasks: Classification, General Classification, Transductive Learning

The key issues pertaining to collection of epidemic disease data for our
analysis purposes are that it is a labour intensive, time consuming and
expensive process resulting in availability of sparse sample data which we use
to develop prediction models. To address this sparse data issue, we present
novel Incremental Transductive methods to circumvent the data collection
process by applying previously acquired data to provide consistent,
confidence-based labelling alternatives to field survey research. We
investigated various reasoning approaches for semisupervised machine learning
including Bayesian models for labelling data. The results show that using the
proposed methods, we can label instances of data with a class of vector density
at a high level of confidence. By applying the Liberal and Strict Training
Approaches, we provide a labelling and classification alternative to standalone
algorithms. The methods in this paper are components in the process of reducing
the proliferation of the Schistosomiasis disease and its effects.
Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection
Authors: Paulo Pinheiro, Deborah L. McGuinness, Henrique Santos
Date: 2017-04-06 00:00:00
Tasks: Management

Significant efforts have been made to understand and document knowledge
related to scientific measurements. Many of those efforts resulted in one or
more high-quality ontologies that describe some aspects of scientific
measurements, but not in a comprehensive and coherently integrated manner. For
instance, we note that many of these high-quality ontologies are not properly
aligned, and more challenging, that they have different and often conflicting
concepts and approaches for encoding knowledge about empirical measurements. As
a result of this lack of an integrated view, it is often challenging for
scientists to determine whether any two scientific measurements were taken in
semantically compatible manners, thus making it difficult to decide whether
measurements should be analyzed in combination or not. In this paper, we
present the Human-Aware Sensor Network Ontology that is a comprehensive
alignment and integration of a sensing infrastructure ontology and a provenance
ontology. HASNetO has been under development for more than one year, and has
been reviewed, shared and used by multiple scientific communities. The ontology
has been in use to support the data management of a number of large-scale
ecological monitoring activities (observations) and empirical experiments.
Contextual Data Collection for Smart Cities
Authors: Henrique Santos, Vasco Furtado, Paulo Pinheiro, Deborah L. McGuinness
Date: 2017-04-06 00:00:00

As part of Smart Cities initiatives, national, regional and local governments
all over the globe are under the mandate of being more open regarding how they
share their data. Under this mandate, many of these governments are publishing
data under the umbrella of open government data, which includes measurement
data from city-wide sensor networks. Furthermore, many of these data are
published in so-called data portals as documents that may be spreadsheets,
comma-separated value (CSV) data files, or plain documents in PDF or Word
documents. The sharing of these documents may be a convenient way for the data
provider to convey and publish data but it is not the ideal way for data
consumers to reuse the data. For example, the problems of reusing the data may
range from difficulty opening a document that is provided in any format that is
not plain text, to the actual problem of understanding the meaning of each
piece of knowledge inside of the document. Our proposal tackles those
challenges by identifying metadata that has been regarded to be relevant for
measurement data and providing a schema for this metadata. We further leverage
the Human-Aware Sensor Network Ontology (HASNetO) to build an architecture for
data collected in urban environments. We discuss the use of HASNetO and the
supporting infrastructure to manage both data and metadata in support of the
City of Fortaleza, a large metropolitan area in Brazil.
Geometry of Policy Improvement
Authors: Guido Montufar, Johannes Rauh
Date: 2017-04-06 00:00:00
Tasks: Decision Making, LEMMA

We investigate the geometry of optimal memoryless time independent decision
making in relation to the amount of information that the acting agent has about
the state of the system. We show that the expected long term reward, discounted
or per time step, is maximized by policies that randomize among at most $k$
actions whenever at most $k$ world states are consistent with the agent's
observation. Moreover, we show that the expected reward per time step can be
studied in terms of the expected discounted reward. Our main tool is a
geometric version of the policy improvement lemma, which identifies a
polyhedral cone of policy changes in which the state value function increases
for all states.
Learning to Generate Reviews and Discovering Sentiment
Authors: Alec Radford, Rafal Jozefowicz, Ilya Sutskever
Date: 2017-04-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: Sentiment Analysis, Subjectivity Analysis

We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment analysis. These representations, learned in an unsupervised
manner, achieve state of the art on the binary subset of the Stanford Sentiment
Treebank. They are also very data efficient. When using only a handful of
labeled examples, our approach matches the performance of strong baselines
trained on full datasets. We also demonstrate the sentiment unit has a direct
influence on the generative process of the model. Simply fixing its value to be
positive or negative generates samples with the corresponding positive or
negative sentiment.
Estimation of solar irradiance using ground-based whole sky imagers
Authors: Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, Stefan Winkler
Date: 2016-06-08 00:00:00

Ground-based whole sky imagers (WSIs) can provide localized images of the sky
of high temporal and spatial resolution, which permits fine-grained cloud
observation. In this paper, we show how images taken by WSIs can be used to
estimate solar radiation. Sky cameras are useful here because they provide
additional information about cloud movement and coverage, which are otherwise
not available from weather station data. Our setup includes ground-based
weather stations at the same location as the imagers. We use their measurements
to validate our methods.
How to Make an Image More Memorable? A Deep Style Transfer Approach
Authors: Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, Elisa Ricci, Nicu Sebe
Date: 2017-04-06 00:00:00
Tasks: Image Generation, Style Transfer

Recent works have shown that it is possible to automatically predict
intrinsic image properties like memorability. In this paper, we take a step
forward addressing the question: "Can we make an image more memorable?".
Methods for automatically increasing image memorability would have an impact in
many application fields like education, gaming or advertising. Our work is
inspired by the popular editing-by-applying-filters paradigm adopted in photo
editing applications, like Instagram and Prisma. In this context, the problem
of increasing image memorability maps to that of retrieving "memorabilizing"
filters or style "seeds". Still, users generally have to go through most of the
available filters before finding the desired solution, thus turning the editing
process into a resource and time consuming task. In this work, we show that it
is possible to automatically retrieve the best style seeds for a given image,
thus remarkably reducing the number of human attempts needed to find a good
match. Our approach leverages from recent advances in the field of image
synthesis and adopts a deep architecture for generating a memorable picture
from a given input image and a style seed. Importantly, to automatically select
the best style a novel learning-based solution, also relying on deep models, is
proposed. Our experimental evaluation, conducted on publicly available
benchmarks, demonstrates the effectiveness of the proposed approach for
generating memorable images through automatic style seed selection
Transferrable Plausibility Model - A Probabilistic Interpretation of Mathematical Theory of Evidence
Authors: Mieczysław Kłopotek
Date: 2017-04-06 00:00:00

This paper suggests a new interpretation of the Dempster-Shafer theory in
terms of probabilistic interpretation of plausibility. A new rule of
combination of independent evidence is shown and its preservation of
interpretation is demonstrated.
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Authors: Yin-Lam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone
Date: 2015-12-05 00:00:00
Tasks: Decision Making, Marketing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

In many sequential decision-making problems one is interested in minimizing
an expected cumulative cost while taking into account \emph{risk}, i.e.,
increased awareness of events of small probability and high consequences.
Accordingly, the objective of this paper is to present efficient reinforcement
learning algorithms for risk-constrained Markov decision processes (MDPs),
where risk is represented via a chance constraint or a constraint on the
conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer
to such problems as percentile risk-constrained MDPs.
  Specifically, we first derive a formula for computing the gradient of the
Lagrangian function for percentile risk-constrained MDPs. Then, we devise
policy gradient and actor-critic algorithms that (1) estimate such gradient,
(2) update the policy in the descent direction, and (3) update the Lagrange
multiplier in the ascent direction. For these algorithms we prove convergence
to locally optimal policies. Finally, we demonstrate the effectiveness of our
algorithms in an optimal stopping problem and an online marketing application.
A Homological Theory of Functions
Authors: Greg Yang
Date: 2017-01-09 00:00:00
Tasks: LEMMA

In computational complexity, a complexity class is given by a set of problems
or functions, and a basic challenge is to show separations of complexity
classes $A \not= B$ especially when $A$ is known to be a subset of $B$. In this
paper we introduce a homological theory of functions that can be used to
establish complexity separations, while also providing other interesting
consequences. We propose to associate a topological space $S_A$ to each class
of functions $A$, such that, to separate complexity classes $A \subseteq B'$,
it suffices to observe a change in "the number of holes", i.e. homology, in
$S_A$ as a subclass $B$ of $B'$ is added to $A$. In other words, if the
homologies of $S_A$ and $S_{A \cup B}$ are different, then $A \not= B'$. We
develop the underlying theory of functions based on combinatorial and
homological commutative algebra and Stanley-Reisner theory, and recover Minsky
and Papert's 1969 result that parity cannot be computed by nonmaximal degree
polynomial threshold functions. In the process, we derive a "maximal principle"
for polynomial threshold functions that is used to extend this result further
to arbitrary symmetric functions. A surprising coincidence is demonstrated,
where the maximal dimension of "holes" in $S_A$ upper bounds the VC dimension
of $A$, with equality for common computational cases such as the class of
polynomial threshold functions or the class of linear functionals in $\mathbb
F_2$, or common algebraic cases such as when the Stanley-Reisner ring of $S_A$
is Cohen-Macaulay. As another interesting application of our theory, we prove a
result that a priori has nothing to do with complexity separation: it
characterizes when a vector subspace intersects the positive cone, in terms of
homological conditions. By analogy to Farkas' result doing the same with
*linear conditions*, we call our theorem the Homological Farkas Lemma.
Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing
Authors: Ke Li, Jitendra Malik
Date: 2015-12-01 00:00:00

Existing methods for retrieving k-nearest neighbours suffer from the curse of
dimensionality. We argue this is caused in part by inherent deficiencies of
space partitioning, which is the underlying strategy used by most existing
methods. We devise a new strategy that avoids partitioning the vector space and
present a novel randomized algorithm that runs in time linear in dimensionality
of the space and sub-linear in the intrinsic dimensionality and the size of the
dataset and takes space constant in dimensionality of the space and linear in
the size of the dataset. The proposed algorithm allows fine-grained control
over accuracy and speed on a per-query basis, automatically adapts to
variations in data density, supports dynamic updates to the dataset and is
easy-to-implement. We show appealing theoretical properties and demonstrate
empirically that the proposed algorithm outperforms locality-sensitivity
hashing (LSH) in terms of approximation quality, speed and space efficiency.
Beyond triplet loss: a deep quadruplet network for person re-identification
Authors: Weihua Chen, Xiaotang Chen, Jian-Guo Zhang, Kaiqi Huang
Date: 2017-04-06 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Re-Identification, Triplet

Person re-identification (ReID) is an important task in wide area video
surveillance which focuses on identifying people across different cameras.
Recently, deep learning networks with a triplet loss become a common framework
for person ReID. However, the triplet loss pays main attentions on obtaining
correct orders on the training set. It still suffers from a weaker
generalization capability from the training set to the testing set, thus
resulting in inferior performance. In this paper, we design a quadruplet loss,
which can lead to the model output with a larger inter-class variation and a
smaller intra-class variation compared to the triplet loss. As a result, our
model has a better generalization ability and can achieve a higher performance
on the testing set. In particular, a quadruplet deep network using a
margin-based online hard negative mining is proposed based on the quadruplet
loss for the person ReID. In extensive experiments, the proposed network
outperforms most of the state-of-the-art algorithms on representative datasets
which clearly demonstrates the effectiveness of our proposed method.
Action Representation Using Classifier Decision Boundaries
Authors: Jue Wang, Anoop Cherian, Fatih Porikli, Stephen Gould
Date: 2017-04-06 00:00:00
Tasks: Action Recognition, Multiple Instance Learning, Temporal Action Localization

Most popular deep learning based models for action recognition are designed
to generate separate predictions within their short temporal windows, which are
often aggregated by heuristic means to assign an action label to the full video
segment. Given that not all frames from a video characterize the underlying
action, pooling schemes that impose equal importance to all frames might be
unfavorable. In an attempt towards tackling this challenge, we propose a novel
pooling scheme, dubbed SVM pooling, based on the notion that among the bag of
features generated by a CNN on all temporal windows, there is at least one
feature that characterizes the action. To this end, we learn a decision
hyperplane that separates this unknown yet useful feature from the rest.
Applying multiple instance learning in an SVM setup, we use the parameters of
this separating hyperplane as a descriptor for the video. Since these
parameters are directly related to the support vectors in a max-margin
framework, they serve as robust representations for pooling of the CNN
features. We devise a joint optimization objective and an efficient solver that
learns these hyperplanes per video and the corresponding action classifiers
over the hyperplanes. Showcased experiments on the standard HMDB and UCF101
datasets demonstrate state-of-the-art performance.
Ordered Pooling of Optical Flow Sequences for Action Recognition
Authors: Jue Wang, Anoop Cherian, Fatih Porikli
Date: 2017-01-12 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Training of Convolutional Neural Networks (CNNs) on long video sequences is
computationally expensive due to the substantial memory requirements and the
massive number of parameters that deep architectures demand. Early fusion of
video frames is thus a standard technique, in which several consecutive frames
are first agglomerated into a compact representation, and then fed into the CNN
as an input sample. For this purpose, a summarization approach that represents
a set of consecutive RGB frames by a single dynamic image to capture pixel
dynamics is proposed recently. In this paper, we introduce a novel ordered
representation of consecutive optical flow frames as an alternative and argue
that this representation captures the action dynamics more effectively than RGB
frames. We provide intuitions on why such a representation is better for action
recognition. We validate our claims on standard benchmark datasets and
demonstrate that using summaries of flow images lead to significant
improvements over RGB frames while achieving accuracy comparable to the
state-of-the-art on UCF101 and HMDB datasets.
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
Authors: Sejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic, Eric Vigoda
Date: 2017-04-06 00:00:00

The Gibbs sampler is a particularly popular Markov chain used for learning
and inference problems in Graphical Models (GMs). These tasks are
computationally intractable in general, and the Gibbs sampler often suffers
from slow mixing. In this paper, we study the Swendsen-Wang dynamics which is a
more sophisticated Markov chain designed to overcome bottlenecks that impede
the Gibbs sampler. We prove O(\log n) mixing time for attractive binary
pairwise GMs (i.e., ferromagnetic Ising models) on stochastic partitioned
graphs having n vertices, under some mild conditions, including low temperature
regions where the Gibbs sampler provably mixes exponentially slow. Our
experiments also confirm that the Swendsen-Wang sampler significantly
outperforms the Gibbs sampler when they are used for learning parameters of
attractive GMs.
A Syntactic Neural Model for General-Purpose Code Generation
Authors: Pengcheng Yin, Graham Neubig
Date: 2017-04-06 00:00:00
Proceeding: ACL 2017 7
Tasks: Code Generation, Semantic Parsing, Text Generation

We consider the problem of parsing natural language descriptions into source
code written in a general-purpose programming language like Python. Existing
data-driven methods treat this problem as a language generation task without
considering the underlying syntax of the target programming language. Informed
by previous work in semantic parsing, in this paper we propose a novel neural
architecture powered by a grammar model to explicitly capture the target syntax
as prior knowledge. Experiments find this an effective way to scale up to
generation of complex programs from natural language descriptions, achieving
state-of-the-art results that well outperform previous code generation and
semantic parsing approaches.