pwc-archive/papers-with-abstracts huggingface dataset:
Minimax Lower Bounds for Kronecker-Structured Dictionary Learning
Authors: Zahra Shakeri, Waheed U. Bajwa, Anand D. Sarwate
Date: 2016-05-17 00:00:00
Tasks: Dictionary Learning

Dictionary learning is the problem of estimating the collection of atomic
elements that provide a sparse representation of measured/collected signals or
data. This paper finds fundamental limits on the sample complexity of
estimating dictionaries for tensor data by proving a lower bound on the minimax
risk. This lower bound depends on the dimensions of the tensor and parameters
of the generative model. The focus of this paper is on second-order tensor
data, with the underlying dictionaries constructed by taking the Kronecker
product of two smaller dictionaries and the observed data generated by sparse
linear combinations of dictionary atoms observed through white Gaussian noise.
In this regard, the paper provides a general lower bound on the minimax risk
and also adapts the proof techniques for equivalent results using sparse and
Gaussian coefficient models. The reported results suggest that the sample
complexity of dictionary learning for tensor data can be significantly lower
than that for unstructured data.
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Authors: Anjith George, Aurobinda Routray
Date: 2016-05-17 00:00:00

Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.
Real-time Eye Gaze Direction Classification Using Convolutional Neural Network
Authors: Anjith George, Aurobinda Routray
Date: 2016-05-17 00:00:00
Tasks: Classification, General Classification

Estimation eye gaze direction is useful in various human-computer interaction
tasks. Knowledge of gaze direction can give valuable information regarding
users point of attention. Certain patterns of eye movements known as eye
accessing cues are reported to be related to the cognitive processes in the
human brain. We propose a real-time framework for the classification of eye
gaze direction and estimation of eye accessing cues. In the first stage, the
algorithm detects faces using a modified version of the Viola-Jones algorithm.
A rough eye region is obtained using geometric relations and facial landmarks.
The eye region obtained is used in the subsequent stage to classify the eye
gaze direction. A convolutional neural network is employed in this work for the
classification of eye gaze direction. The proposed algorithm was tested on Eye
Chimera database and found to outperform state of the art methods. The
computational complexity of the algorithm is very less in the testing phase.
The algorithm achieved an average frame rate of 24 fps in the desktop
environment.
Biologically Inspired Radio Signal Feature Extraction with Sparse Denoising Autoencoders
Authors: Benjamin Migliori, Riley Zeller-Townson, Daniel Grady, Daniel Gebhardt
Date: 2016-05-17 00:00:00
Tasks: Classification, Denoising, General Classification

Automatic modulation classification (AMC) is an important task for modern
communication systems; however, it is a challenging problem when signal
features and precise models for generating each modulation may be unknown. We
present a new biologically-inspired AMC method without the need for models or
manually specified features --- thus removing the requirement for expert prior
knowledge. We accomplish this task using regularized stacked sparse denoising
autoencoders (SSDAs). Our method selects efficient classification features
directly from raw in-phase/quadrature (I/Q) radio signals in an unsupervised
manner. These features are then used to construct higher-complexity abstract
features which can be used for automatic modulation classification. We
demonstrate this process using a dataset generated with a software defined
radio, consisting of random input bits encoded in 100-sample segments of
various common digital radio modulations. Our results show correct
classification rates of > 99% at 7.5 dB signal-to-noise ratio (SNR) and > 92%
at 0 dB SNR in a 6-way classification test. Our experiments demonstrate a
dramatically new and broadly applicable mechanism for performing AMC and
related tasks without the need for expert-defined or modulation-specific signal
information.
On the boosting ability of top-down decision tree learning algorithm for multiclass classification
Authors: Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski
Date: 2016-05-17 00:00:00
Tasks: General Classification

We analyze the performance of the top-down multiclass classification
algorithm for decision tree learning called LOMtree, recently proposed in the
literature Choromanska and Langford (2014) for solving efficiently
classification problems with very large number of classes. The algorithm online
optimizes the objective function which simultaneously controls the depth of the
tree and its statistical accuracy. We prove important properties of this
objective and explore its connection to three well-known entropy-based decision
tree objectives, i.e. Shannon entropy, Gini-entropy and its modified version,
for which instead online optimization schemes were not yet developed. We show,
via boosting-type guarantees, that maximizing the considered objective leads
also to the reduction of all of these entropy-based objectives. The bounds we
obtain critically depend on the strong-concavity properties of the
entropy-based criteria, where the mildest dependence on the number of classes
(only logarithmic) corresponds to the Shannon entropy.
Combinatorially Generated Piecewise Activation Functions
Authors: Justin Chen
Date: 2016-05-17 00:00:00
Tasks: Model Selection

In the neuroevolution literature, research has primarily focused on evolving
the number of nodes, connections, and weights in artificial neural networks.
Few attempts have been made to evolve activation functions. Research in
evolving activation functions has mainly focused on evolving function
parameters, and developing heterogeneous networks by selecting from a fixed
pool of activation functions. This paper introduces a novel technique for
evolving heterogeneous artificial neural networks through combinatorially
generating piecewise activation functions to enhance expressive power. I
demonstrate this technique on NeuroEvolution of Augmenting Topologies using
ArcTan and Sigmoid, and show that it outperforms the original algorithm on
non-Markovian double pole balancing. This technique expands the landscape of
unconventional activation functions by demonstrating that they are competitive
with canonical choices, and introduces a purview for further exploration of
automatic model selection for artificial neural networks.
Multimodal Sparse Coding for Event Detection
Authors: Youngjune Gwon, William Campbell, Kevin Brady, Douglas Sturim, Miriam Cha, H. T. Kung
Date: 2016-05-17 00:00:00
Tasks: Classification, Event Detection, General Classification

Unsupervised feature learning methods have proven effective for
classification tasks based on a single modality. We present multimodal sparse
coding for learning feature representations shared across multiple modalities.
The shared representations are applied to multimedia event detection (MED) and
evaluated in comparison to unimodal counterparts, as well as other feature
learning methods such as GMM supervectors and sparse RBM. We report the
cross-validated classification accuracy and mean average precision of the MED
system trained on features learned from our unimodal and multimodal settings
for a subset of the TRECVID MED 2014 dataset.
Tweet2Vec: Character-Based Distributed Representations for Social Media
Authors: Bhuwan Dhingra, Zhong Zhou, Dylan Fitzpatrick, Michael Muehl, William W. Cohen
Date: 2016-05-11 00:00:00
Proceeding: ACL 2016 8

Text from social media provides a set of challenges that can cause
traditional NLP approaches to fail. Informal language, spelling errors,
abbreviations, and special characters are all commonplace in these posts,
leading to a prohibitively large vocabulary size for word-level approaches. We
propose a character composition model, tweet2vec, which finds vector-space
representations of whole tweets by learning complex, non-local dependencies in
character sequences. The proposed model outperforms a word-level baseline at
predicting user-annotated hashtags associated with the posts, doing
significantly better when the input contains many out-of-vocabulary words or
unusual character sequences. Our tweet2vec encoder is publicly available.
Enhanced Twitter Sentiment Classification Using Contextual Information
Authors: Soroush Vosoughi, Helen Zhou, Deb Roy
Date: 2016-05-17 00:00:00
Proceeding: WS 2015 9
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Authors: Yongchao Xu, Thierry Géraud, Laurent Najman
Date: 2016-03-15 00:00:00
Tasks: Attribute, Segmentation

Hierarchies, such as the tree of shapes, are popular representations for
image simplification and segmentation thanks to their multiscale structures.
Selecting meaningful level lines (boundaries of shapes) yields to simplify
image while preserving intact salient structures. Many image simplification and
segmentation methods are driven by the optimization of an energy functional,
for instance the celebrated Mumford-Shah functional. In this paper, we propose
an efficient approach to hierarchical image simplification and segmentation
based on the minimization of the piecewise-constant Mumford-Shah functional.
This method conforms to the current trend that consists in producing
hierarchical results rather than a unique partition. Contrary to classical
approaches which compute optimal hierarchical segmentations from an input
hierarchy of segmentations, we rely on the tree of shapes, a unique and
well-defined representation equivalent to the image. Simply put, we compute for
each level line of the image an attribute function that characterizes its
persistence under the energy minimization. Then we stack the level lines from
meaningless ones to salient ones through a saliency map based on extinction
values defined on the tree-based shape space. Qualitative illustrations and
quantitative evaluation on Weizmann segmentation evaluation database
demonstrate the state-of-the-art performance of our method.
Structured Prediction of 3D Human Pose with Deep Neural Networks
Authors: Bugra Tekin, Isinsu Katircioglu, Mathieu Salzmann, Vincent Lepetit, Pascal Fua
Date: 2016-05-17 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Deep Learning, Image to 3D, Pose Estimation, regression, Structured Prediction

Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
They either train a Convolutional Neural Network to directly regress from image
to 3D pose, which ignores the dependencies between human joints, or model these
dependencies via a max-margin structured learning framework, which involves a
high computational cost at inference time.
  In this paper, we introduce a Deep Learning regression architecture for
structured prediction of 3D human pose from monocular images that relies on an
overcomplete auto-encoder to learn a high-dimensional latent pose
representation and account for joint dependencies. We demonstrate that our
approach outperforms state-of-the-art ones both in terms of structure
preservation and prediction accuracy.
Sentence Pair Scoring: Towards Unified Framework for Text Comprehension
Authors: Petr Baudiš, Jan Pichl, Tomáš Vyskočil, Jan Šedivý
Date: 2016-03-19 00:00:00
Tasks: Natural Language Inference, Reading Comprehension, Sentence

We review the task of Sentence Pair Scoring, popular in the literature in
various forms - viewed as Answer Sentence Selection, Semantic Text Scoring,
Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a
component of Memory Networks.
  We argue that all such tasks are similar from the model perspective and
propose new baselines by comparing the performance of common IR metrics and
popular convolutional, recurrent and attention-based neural models across many
Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating
randomized models, propose a statistically grounded methodology, and attempt to
improve comparisons by releasing new datasets that are much harder than some of
the currently used well explored benchmarks. We introduce a unified open source
software framework with easily pluggable models and tasks, which enables us to
experiment with multi-task reusability of trained sentence model. We set a new
state-of-art in performance on the Ubuntu Dialogue dataset.
Digital Stylometry: Linking Profiles Across Social Networks
Authors: Soroush Vosoughi, Helen Zhou, Deb Roy
Date: 2016-05-17 00:00:00

There is an ever growing number of users with accounts on multiple social
media and networking sites. Consequently, there is increasing interest in
matching user accounts and profiles across different social networks in order
to create aggregate profiles of users. In this paper, we present models for
Digital Stylometry, which is a method for matching users through stylometry
inspired techniques. We experimented with linguistic, temporal, and combined
temporal-linguistic models for matching user accounts, using standard and novel
techniques. Using publicly available data, our best model, a combined
temporal-linguistic one, was able to correctly match the accounts of 31% of
5,612 distinct users across Twitter and Facebook.
Tweet Acts: A Speech Act Classifier for Twitter
Authors: Soroush Vosoughi, Deb Roy
Date: 2016-05-17 00:00:00
Tasks: General Classification, Multi-class Classification, regression

Speech acts are a way to conceptualize speech as action. This holds true for
communication on any platform, including social media platforms such as
Twitter. In this paper, we explored speech act recognition on Twitter by
treating it as a multi-class classification problem. We created a taxonomy of
six speech acts for Twitter and proposed a set of semantic and syntactic
features. We trained and tested a logistic regression classifier using a data
set of manually labelled tweets. Our method achieved a state-of-the-art
performance with an average F1 score of more than $0.70$. We also explored
classifiers with three different granularities (Twitter-wide, type-specific and
topic-specific) in order to find the right balance between generalization and
overfitting for our task.
Automatic Detection and Categorization of Election-Related Tweets
Authors: Prashanth Vijayaraghavan, Soroush Vosoughi, Deb Roy
Date: 2016-05-17 00:00:00
Tasks: Diversity

With the rise in popularity of public social media and micro-blogging
services, most notably Twitter, the people have found a venue to hear and be
heard by their peers without an intermediary. As a consequence, and aided by
the public nature of Twitter, political scientists now potentially have the
means to analyse and understand the narratives that organically form, spread
and decline among the public in a political campaign. However, the volume and
diversity of the conversation on Twitter, combined with its noisy and
idiosyncratic nature, make this a hard task. Thus, advanced data mining and
language processing techniques are required to process and analyse the data. In
this paper, we present and evaluate a technical framework, based on recent
advances in deep neural networks, for identifying and analysing
election-related conversation on Twitter on a continuous, longitudinal basis.
Our models can detect election-related tweets with an F-score of 0.92 and can
categorize these tweets into 22 topics with an F-score of 0.90.
Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate
Authors: Santosh Tirunagari, Simon Bull, Norman Poh
Date: 2016-05-17 00:00:00
Tasks: General Classification, GPR, Kidney Function, Time Series, Time Series Analysis

A patient's estimated glomerular filtration rate (eGFR) can provide important
information about disease progression and kidney function. Traditionally, an
eGFR time series is interpreted by a human expert labelling it as stable or
unstable. While this approach works for individual patients, the time consuming
nature of it precludes the quick evaluation of risk in large numbers of
patients. However, automating this process poses significant challenges as eGFR
measurements are usually recorded at irregular intervals and the series of
measurements differs in length between patients. Here we present a two-tier
system to automatically classify an eGFR trend. First, we model the time series
using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed
size vector of fifty time-dependent observations. Second, we classify the
resampled eGFR time series using a K-NN/SVM classifier, and evaluate its
performance via 5-fold cross validation. Using this approach we achieved an
F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst
themselves.
A Semi-automatic Method for Efficient Detection of Stories on Social Media
Authors: Soroush Vosoughi, Deb Roy
Date: 2016-05-17 00:00:00

Twitter has become one of the main sources of news for many people. As
real-world events and emergencies unfold, Twitter is abuzz with hundreds of
thousands of stories about the events. Some of these stories are harmless,
while others could potentially be life-saving or sources of malicious rumors.
Thus, it is critically important to be able to efficiently track stories that
spread on Twitter during these events. In this paper, we present a novel
semi-automatic tool that enables users to efficiently identify and track
stories about real-world events on Twitter. We ran a user study with 25
participants, demonstrating that compared to more conventional methods, our
tool can increase the speed and the accuracy with which users can track stories
about real-world events.
Recurrent Neural Network for Text Classification with Multi-Task Learning
Authors: Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Date: 2016-05-17 00:00:00
Tasks: Classification, Emotion Recognition in Conversation, General Classification, Multi-Task Learning, text-classification, Text Classification

Neural network based methods have obtained great progress on a variety of
natural language processing tasks. However, in most previous works, the models
are learned based on single-task supervised objectives, which often suffer from
insufficient training data. In this paper, we use the multi-task learning
framework to jointly learn across multiple related tasks. Based on recurrent
neural network, we propose three different mechanisms of sharing information to
model text with task-specific and shared layers. The entire network is trained
jointly on all these tasks. Experiments on four benchmark text classification
tasks show that our proposed models can improve the performance of a task with
the help of other related tasks.
HARRISON: A Benchmark on HAshtag Recommendation for Real-world Images in Social Networks
Authors: Minseok Park, Hanxiang Li, Junmo Kim
Date: 2016-05-17 00:00:00

Simple, short, and compact hashtags cover a wide range of information on
social networks. Although many works in the field of natural language
processing (NLP) have demonstrated the importance of hashtag recommendation,
hashtag recommendation for images has barely been studied. In this paper, we
introduce the HARRISON dataset, a benchmark on hashtag recommendation for real
world images in social networks. The HARRISON dataset is a realistic dataset,
composed of 57,383 photos from Instagram and an average of 4.5 associated
hashtags for each photo. To evaluate our dataset, we design a baseline
framework consisting of visual feature extractor based on convolutional neural
network (CNN) and multi-label classifier based on neural network. Based on this
framework, two single feature-based models, object-based and scene-based model,
and an integrated model of them are evaluated on the HARRISON dataset. Our
dataset shows that hashtag recommendation task requires a wide and contextual
understanding of the situation conveyed in the image. As far as we know, this
work is the first vision-only attempt at hashtag recommendation for real world
images in social networks. We expect this benchmark to accelerate the
advancement of hashtag recommendation.
DehazeNet: An End-to-End System for Single Image Haze Removal
Authors: Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, DaCheng Tao
Date: 2016-01-28 00:00:00
Tasks: Image Dehazing, Single Image Haze Removal

Single image haze removal is a challenging ill-posed problem. Existing
methods use various constraints/priors to get plausible dehazing solutions. The
key to achieve haze removal is to estimate a medium transmission map for an
input hazy image. In this paper, we propose a trainable end-to-end system
called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy
image as input, and outputs its medium transmission map that is subsequently
used to recover a haze-free image via atmospheric scattering model. DehazeNet
adopts Convolutional Neural Networks (CNN) based deep architecture, whose
layers are specially designed to embody the established assumptions/priors in
image dehazing. Specifically, layers of Maxout units are used for feature
extraction, which can generate almost all haze-relevant features. We also
propose a novel nonlinear activation function in DehazeNet, called Bilateral
Rectified Linear Unit (BReLU), which is able to improve the quality of
recovered haze-free image. We establish connections between components of the
proposed DehazeNet and those used in existing methods. Experiments on benchmark
images show that DehazeNet achieves superior performance over existing methods,
yet keeps efficient and easy to use.