pwc-archive/papers-with-abstracts huggingface dataset:
Molding CNNs for text: non-linear, non-consecutive convolutions
Authors: Tao Lei, Regina Barzilay, Tommi Jaakkola
Date: 2015-08-17 00:00:00
Proceeding: EMNLP 2015 9
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, tensor algebra

The success of deep learning often derives from well-chosen operational
building blocks. In this work, we revise the temporal convolution operation in
CNNs to better adapt it to text processing. Instead of concatenating word
representations, we appeal to tensor algebra and use low-rank n-gram tensors to
directly exploit interactions between words already at the convolution stage.
Moreover, we extend the n-gram convolution to non-consecutive words to
recognize patterns with intervening words. Through a combination of low-rank
tensors, and pattern weighting, we can efficiently evaluate the resulting
convolution operation via dynamic programming. We test the resulting
architecture on standard sentiment classification and news categorization
tasks. Our model achieves state-of-the-art performance both in terms of
accuracy and training speed. For instance, we obtain 51.2% accuracy on the
fine-grained sentiment classification task.
Low Rank Representation on Riemannian Manifold of Square Root Densities
Authors: Yifan Fu, Junbin Gao, Xia Hong, David Tien
Date: 2015-08-18 00:00:00
Tasks: Clustering, General Classification

In this paper, we present a novel low rank representation (LRR) algorithm for
data lying on the manifold of square root densities. Unlike traditional LRR
methods which rely on the assumption that the data points are vectors in the
Euclidean space, our new algorithm is designed to incorporate the intrinsic
geometric structure and geodesic distance of the manifold. Experiments on
several computer vision datasets showcase its noise robustness and superior
performance on classification and subspace clustering compared to other
state-of-the-art approaches.
Action Recognition based on Subdivision-Fusion Model
Authors: Hao Zongbo, Lu Linlin, Zhang Qianni, Wu Jie, Izquierdo Ebroul, Yang Juanyu, Zhao Jun
Date: 2015-08-18 00:00:00
Tasks: Action Recognition, model, Temporal Action Localization

This paper proposes a novel Subdivision-Fusion Model (SFM) to recognize human
actions. In most action recognition tasks, overlapping feature distribution is
a common problem leading to overfitting. In the subdivision stage of the
proposed SFM, samples in each category are clustered. Then, such samples are
grouped into multiple more concentrated subcategories. Boundaries for the
subcategories are easier to find and as consequence overfitting is avoided. In
the subsequent fusion stage, the multi-subcategories classification results are
converted back to the original category recognition problem. Two methods to
determine the number of clusters are provided. The proposed model has been
thoroughly tested with four popular datasets. In the Hollywood2 dataset, an
accuracy of 79.4% is achieved, outperforming the state-of-the-art accuracy of
64.3%. The performance on the YouTube Action dataset has been improved from
75.8% to 82.5%, while considerably improvements are also observed on the KTH
and UCF50 datasets.
Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data
Authors: Haroon Raja, Waheed U. Bajwa
Date: 2014-12-25 00:00:00
Tasks: Dictionary Learning

This paper studies the problem of data-adaptive representations for big,
distributed data. It is assumed that a number of geographically-distributed,
interconnected sites have massive local data and they are interested in
collaboratively learning a low-dimensional geometric structure underlying these
data. In contrast to previous works on subspace-based data representations,
this paper focuses on the geometric structure of a union of subspaces (UoS). In
this regard, it proposes a distributed algorithm---termed cloud K-SVD---for
collaborative learning of a UoS structure underlying distributed data of
interest. The goal of cloud K-SVD is to learn a common overcomplete dictionary
at each individual site such that every sample in the distributed data can be
represented through a small number of atoms of the learned dictionary. Cloud
K-SVD accomplishes this goal without requiring exchange of individual samples
between sites. This makes it suitable for applications where sharing of raw
data is discouraged due to either privacy concerns or large volumes of data.
This paper also provides an analysis of cloud K-SVD that gives insights into
its properties as well as deviations of the dictionaries learned at individual
sites from a centralized solution in terms of different measures of
local/global data and topology of interconnections. Finally, the paper
numerically illustrates the efficacy of cloud K-SVD on real and synthetic
distributed data.
Reflective Oracles: A Foundation for Classical Game Theory
Authors: Benja Fallenstein, Jessica Taylor, Paul F. Christiano
Date: 2015-08-17 00:00:00

Classical game theory treats players as special---a description of a game
contains a full, explicit enumeration of all players---even though in the real
world, "players" are no more fundamentally special than rocks or clouds. It
isn't trivial to find a decision-theoretic foundation for game theory in which
an agent's coplayers are a non-distinguished part of the agent's environment.
Attempts to model both players and the environment as Turing machines, for
example, fail for standard diagonalization reasons.
  In this paper, we introduce a "reflective" type of oracle, which is able to
answer questions about the outputs of oracle machines with access to the same
oracle. These oracles avoid diagonalization by answering some queries randomly.
We show that machines with access to a reflective oracle can be used to define
rational agents using causal decision theory. These agents model their
environment as a probabilistic oracle machine, which may contain other agents
as a non-distinguished part.
  We show that if such agents interact, they will play a Nash equilibrium, with
the randomization in mixed strategies coming from the randomization in the
oracle's answers. This can be seen as providing a foundation for classical game
theory in which players aren't special.
Evaluating Classifiers in Detecting 419 Scams in Bilingual Cybercriminal Communities
Authors: Alex V. Mbaziira, Ehab Abozinadah, James H. Jones Jr
Date: 2015-08-17 00:00:00

Incidents of organized cybercrime are rising because of criminals are reaping
high financial rewards while incurring low costs to commit crime. As the
digital landscape broadens to accommodate more internet-enabled devices and
technologies like social media, more cybercriminals who are not native English
speakers are invading cyberspace to cash in on quick exploits. In this paper we
evaluate the performance of three machine learning classifiers in detecting 419
scams in a bilingual Nigerian cybercriminal community. We use three popular
classifiers in text processing namely: Na\"ive Bayes, k-nearest neighbors (IBK)
and Support Vector Machines (SVM). The preliminary results on a real world
dataset reveal the SVM significantly outperforms Na\"ive Bayes and IBK at 95%
confidence level.
EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video
Authors: Guangnan Ye, Yitong Li, Hongliang Xu, Dong Liu, Shih-Fu Chang
Date: 2015-06-08 00:00:00
Tasks: Articles, Event Detection, Retrieval

Event-specific concepts are the semantic concepts designed for the events of
interest, which can be used as a mid-level representation of complex events in
videos. Existing methods only focus on defining event-specific concepts for a
small number of predefined events, but cannot handle novel unseen events. This
motivates us to build a large scale event-specific concept library that covers
as many real-world events and their concepts as possible. Specifically, we
choose WikiHow, an online forum containing a large number of how-to articles on
human daily life events. We perform a coarse-to-fine event discovery process
and discover 500 events from WikiHow articles. Then we use each event name as
query to search YouTube and discover event-specific concepts from the tags of
returned videos. After an automatic filter process, we end up with 95,321
videos and 4,490 concepts. We train a Convolutional Neural Network (CNN) model
on the 95,321 videos over the 500 events, and use the model to extract deep
learning feature from video content. With the learned deep learning feature, we
train 4,490 binary SVM classifiers as the event-specific concept library. The
concepts and events are further organized in a hierarchical structure defined
by WikiHow, and the resultant concept library is called EventNet. Finally, the
EventNet concept library is used to generate concept based representation of
event videos. To the best of our knowledge, EventNet represents the first video
event ontology that organizes events and their concepts into a semantic
structure. It offers great potential for event retrieval and browsing.
Extensive experiments over the zero-shot event retrieval task when no training
samples are available show that the EventNet concept library consistently and
significantly outperforms the state-of-the-art (such as the 20K ImageNet
concepts trained with CNN) by a large margin up to 207%.
A Deep Learning Approach to Structured Signal Recovery
Authors: Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk
Date: 2015-08-17 00:00:00
Tasks: Compressive Sensing, Deep Learning, Denoising

In this paper, we develop a new framework for sensing and recovering
structured signals. In contrast to compressive sensing (CS) systems that employ
linear measurements, sparse representations, and computationally complex
convex/greedy algorithms, we introduce a deep learning framework that supports
both linear and mildly nonlinear measurements, that learns a structured
representation from training data, and that efficiently computes a signal
estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an
unsupervised feature learner. SDA enables us to capture statistical
dependencies between the different elements of certain signals and improve
signal recovery performance as compared to the CS approach.
A Generative Model for Multi-Dialect Representation
Authors: Emmanuel N. Osegi
Date: 2015-08-17 00:00:00
Tasks: model

In the era of deep learning several unsupervised models have been developed
to capture the key features in unlabeled handwritten data. Popular among them
is the Restricted Boltzmann Machines RBM. However, due to the novelty in
handwritten multidialect data, the RBM may fail to generate an efficient
representation. In this paper we propose a generative model, the Mode
Synthesizing Machine MSM for on-line representation of real life handwritten
multidialect language data. The MSM takes advantage of the hierarchical
representation of the modes of a data distribution using a two-point error
update to learn a sequence of representative multidialects in a generative way.
Experiments were performed to evaluate the performance of the MSM over the RBM
with the former attaining much lower error values than the latter on both
independent and mixed data set.
A complex network approach to stylometry
Authors: Diego R. Amancio
Date: 2015-06-30 00:00:00

Statistical methods have been widely employed to study the fundamental
properties of language. In recent years, methods from complex and dynamical
systems proved useful to create several language models. Despite the large
amount of studies devoted to represent texts with physical models, only a
limited number of studies have shown how the properties of the underlying
physical systems can be employed to improve the performance of natural language
processing tasks. In this paper, I address this problem by devising complex
networks methods that are able to improve the performance of current
statistical methods. Using a fuzzy classification strategy, I show that the
topological properties extracted from texts complement the traditional textual
description. In several cases, the performance obtained with hybrid approaches
outperformed the results obtained when only traditional or networked methods
were used. Because the proposed model is generic, the framework devised here
could be straightforwardly used to study similar textual applications where the
topology plays a pivotal role in the description of the interacting agents.
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
Authors: Sanghack Lee, Vasant Honavar
Date: 2015-08-10 00:00:00

Maier et al. (2010) introduced the relational causal model (RCM) for
representing and inferring causal relationships in relational data. A lifted
representation, called abstract ground graph (AGG), plays a central role in
reasoning with and learning of RCM. The correctness of the algorithm proposed
by Maier et al. (2013a) for learning RCM from data relies on the soundness and
completeness of AGG for relational d-separation to reduce the learning of an
RCM to learning of an AGG. We revisit the definition of AGG and show that AGG,
as defined in Maier et al. (2013b), does not correctly abstract all ground
graphs. We revise the definition of AGG to ensure that it correctly abstracts
all ground graphs. We further show that AGG representation is not complete for
relational d-separation, that is, there can exist conditional independence
relations in an RCM that are not entailed by AGG. A careful examination of the
relationship between the lack of completeness of AGG for relational
d-separation and faithfulness conditions suggests that weaker notions of
completeness, namely adjacency faithfulness and orientation faithfulness
between an RCM and its AGG, can be used to learn an RCM from data.
Tomographic Image Reconstruction using Training images
Authors: Sara Soltani, Martin S. Andersen, Per Christian Hansen
Date: 2015-03-06 00:00:00
Tasks: Image Reconstruction

We describe and examine an algorithm for tomographic image reconstruction
where prior knowledge about the solution is available in the form of training
images. We first construct a nonnegative dictionary based on prototype elements
from the training images; this problem is formulated as a regularized
non-negative matrix factorization. Incorporating the dictionary as a prior in a
convex reconstruction problem, we then find an approximate solution with a
sparse representation in the dictionary. The dictionary is applied to
non-overlapping patches of the image, which reduces the computational
complexity compared to other algorithms. Computational experiments clarify the
choice and interplay of the model parameters and the regularization parameters,
and we show that in few-projection low-dose settings our algorithm is
competitive with total variation regularization and tends to include more
texture and more correct edges.
Sense Beyond Expressions: Cuteness
Authors: Kang Wang, Tam V. Nguyen, Jiashi Feng, Jose Sepulveda
Date: 2015-08-17 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

With the development of Internet culture, cuteness has become a popular
concept. Many people are curious about what factors making a person look cute.
However, there is rare research to answer this interesting question. In this
work, we construct a dataset of personal images with comprehensively annotated
cuteness scores and facial attributes to investigate this high-level concept in
depth. Based on this dataset, through an automatic attributes mining process,
we find several critical attributes determining the cuteness of a person. We
also develop a novel Continuous Latent Support Vector Machine (C-LSVM) method
to predict the cuteness score of one person given only his image. Extensive
evaluations validate the effectiveness of the proposed method for cuteness
prediction.
LCNN: Low-level Feature Embedded CNN for Salient Object Detection
Authors: Hongyang Li, Huchuan Lu, Zhe Lin, Xiaohui Shen, Brian Price
Date: 2015-08-17 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In this paper, we propose a novel deep neural network framework embedded with
low-level features (LCNN) for salient object detection in complex images. We
utilise the advantage of convolutional neural networks to automatically learn
the high-level features that capture the structured information and semantic
context in the image. In order to better adapt a CNN model into the saliency
task, we redesign the network architecture based on the small-scale datasets.
Several low-level features are extracted, which can effectively capture
contrast and spatial information in the salient regions, and incorporated to
compensate with the learned high-level features at the output of the last fully
connected layer. The concatenated feature vector is further fed into a
hinge-loss SVM detector in a joint discriminative learning manner and the final
saliency score of each region within the bounding box is obtained by the linear
combination of the detector's weights. Experiments on three challenging
benchmark (MSRA-5000, PASCAL-S, ECCSD) demonstrate our algorithm to be
effective and superior than most low-level oriented state-of-the-arts in terms
of P-R curves, F-measure and mean absolute errors.
Using a Machine Learning Approach to Implement and Evaluate Product Line Features
Authors: Davide Bacciu, Stefania Gnesi, Laura Semini
Date: 2015-08-17 00:00:00
Tasks: BIG-bench Machine Learning

Bike-sharing systems are a means of smart transportation in urban
environments with the benefit of a positive impact on urban mobility. In this
paper we are interested in studying and modeling the behavior of features that
permit the end user to access, with her/his web browser, the status of the
Bike-Sharing system. In particular, we address features able to make a
prediction on the system state. We propose to use a machine learning approach
to analyze usage patterns and learn computational models of such features from
logs of system usage.
  On the one hand, machine learning methodologies provide a powerful and
general means to implement a wide choice of predictive features. On the other
hand, trained machine learning models are provided with a measure of predictive
performance that can be used as a metric to assess the cost-performance
trade-off of the feature. This provides a principled way to assess the runtime
behavior of different components before putting them into operation.
Network Routing Optimization Using Swarm Intelligence
Authors: Mohamed A. El Galil
Date: 2012-09-18 00:00:00

The aim of this paper is to highlight and explore a traditional problem,
which is the minimum spanning tree, and finding the shortest-path in network
routing, by using Swarm Intelligence. This work to be considered as an
investigation topic with combination between operations research, discrete
mathematics, and evolutionary computing aiming to solve one of networking
problems.
Discrete Route/Trajectory Decision Making Problems
Authors: Mark Sh. Levin
Date: 2015-08-16 00:00:00
Tasks: Decision Making

The paper focuses on composite multistage decision making problems which are
targeted to design a route/trajectory from an initial decision situation
(origin) to goal (destination) decision situation(s). Automobile routing
problem is considered as a basic physical metaphor. The problems are based on a
discrete (combinatorial) operations/states design/solving space (e.g.,
digraph). The described types of discrete decision making problems can be
considered as intelligent design of a route (trajectory, strategy) and can be
used in many domains: (a) education (planning of student educational
trajectory), (b) medicine (medical treatment), (c) economics (trajectory of
start-up development). Several types of the route decision making problems are
described: (i) basic route decision making, (ii) multi-goal route decision
making, (iii) multi-route decision making, (iv) multi-route decision making
with route/trajectory change(s), (v) composite multi-route decision making
(solution is a composition of several routes/trajectories at several
corresponding domains), and (vi) composite multi-route decision making with
coordinated routes/trajectories. In addition, problems of modeling and building
the design spaces are considered. Numerical examples illustrate the suggested
approach. Three applications are considered: educational trajectory
(orienteering problem), plan of start-up company (modular three-stage design),
and plan of medical treatment (planning over digraph with two-component
vertices).
Two-stage Cascaded Classifier for Purchase Prediction
Authors: Sheikh Muhammad Sarwar, Mahamudul Hasan, Dmitry I. Ignatov
Date: 2015-08-16 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Vocal Bursts Valence Prediction

In this paper we describe our machine learning solution for the RecSys
Challenge, 2015. We have proposed a time efficient two-stage cascaded
classifier for the prediction of buy sessions and purchased items within such
sessions. Based on the model, several interesting features found, and formation
of our own test bed, we have achieved a reasonable score. Usage of Random
Forests helps us to cope with the effect of the multiplicity of good models
depending on varying subsets of features in the purchased items prediction and,
in its turn, boosting is used as a suitable technique to overcome severe class
imbalance of the buy-session prediction.
Online Representation Learning in Recurrent Neural Language Models
Authors: Marek Rei
Date: 2015-08-16 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Language Modelling, Representation Learning

We investigate an extension of continuous online learning in recurrent neural
network language models. The model keeps a separate vector representation of
the current unit of text being processed and adaptively adjusts it after each
prediction. The initial experiments give promising results, indicating that the
method is able to increase language modelling accuracy, while also decreasing
the parameters needed to store the model along with the computation required at
each step.
Talking to the crowd: What do people react to in online discussions?
Authors: Aaron Jaech, Victoria Zayats, Hao Fang, Mari Ostendorf, Hannaneh Hajishirzi
Date: 2015-07-08 00:00:00
Proceeding: EMNLP 2015 9

This paper addresses the question of how language use affects community
reaction to comments in online discussion forums, and the relative importance
of the message vs. the messenger. A new comment ranking task is proposed based
on community annotated karma in Reddit discussions, which controls for topic
and timing of comments. Experimental work with discussion threads from six
subreddits shows that the importance of different types of language features
varies with the community of interest.
What Your Username Says About You
Authors: Aaron Jaech, Mari Ostendorf
Date: 2015-07-08 00:00:00
Proceeding: EMNLP 2015 9

Usernames are ubiquitous on the Internet, and they are often suggestive of
user demographics. This work looks at the degree to which gender and language
can be inferred from a username alone by making use of unsupervised morphology
induction to decompose usernames into sub-units. Experimental results on the
two tasks demonstrate the effectiveness of the proposed morphological features
compared to a character n-gram baseline.