pwc-archive/papers-with-abstracts huggingface dataset:
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Authors: Anh Cat Le Ngo, John See, Raphael Chung-Wei Phan
Date: 2016-01-19 00:00:00
Tasks: Emotion Recognition

Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.
Class Probability Estimation via Differential Geometric Regularization
Authors: Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff
Date: 2015-03-04 00:00:00
Tasks: Classification, General Classification

We study the problem of supervised learning for both binary and multiclass
classification from a unified geometric perspective. In particular, we propose
a geometric regularization technique to find the submanifold corresponding to a
robust estimator of the class probability $P(y|\pmb{x})$. The regularization
term measures the volume of this submanifold, based on the intuition that
overfitting produces rapid local oscillations and hence large volume of the
estimator. This technique can be applied to regularize any classification
function that satisfies two requirements: firstly, an estimator of the class
probability can be obtained; secondly, first and second derivatives of the
class probability estimator can be calculated. In experiments, we apply our
regularization technique to standard loss functions for classification, our
RBF-based implementation compares favorably to widely used regularization
methods for both binary and multiclass classification.
Simple Search Algorithms on Semantic Networks Learned from Language Use
Authors: Aida Nematzadeh, Filip Miscevic, Suzanne Stevenson
Date: 2016-02-10 00:00:00
Tasks: Open-Ended Question Answering, Retrieval

Recent empirical and modeling research has focused on the semantic fluency
task because it is informative about semantic memory. An interesting interplay
arises between the richness of representations in semantic memory and the
complexity of algorithms required to process it. It has remained an open
question whether representations of words and their relations learned from
language use can enable a simple search algorithm to mimic the observed
behavior in the fluency task. Here we show that it is plausible to learn rich
representations from naturalistic data for which a very simple search algorithm
(a random walk) can replicate the human patterns. We suggest that explicitly
structuring knowledge about words into a semantic network plays a crucial role
in modeling human behavior in memory search and retrieval; moreover, this is
the case across a range of semantic information sources.
Attentive Pooling Networks
Authors: Cicero dos Santos, Ming Tan, Bing Xiang, Bo-Wen Zhou
Date: 2016-02-11 00:00:00
Tasks: Answer Selection, Question Answering, Representation Learning

In this work, we propose Attentive Pooling (AP), a two-way attention
mechanism for discriminative model training. In the context of pair-wise
ranking or classification with neural networks, AP enables the pooling layer to
be aware of the current input pair, in a way that information from the two
input items can directly influence the computation of each other's
representations. Along with such representations of the paired inputs, AP
jointly learns a similarity measure over projected segments (e.g. trigrams) of
the pair, and subsequently, derives the corresponding attention vector for each
input to guide the pooling. Our two-way attention mechanism is a general
framework independent of the underlying representation learning, and it has
been applied to both convolutional neural networks (CNNs) and recurrent neural
networks (RNNs) in our studies. The empirical results, from three very
different benchmark tasks of question answering/answer selection, demonstrate
that our proposed models outperform a variety of strong baselines and achieve
state-of-the-art performance in all the benchmarks.
Variations of the Similarity Function of TextRank for Automated Summarization
Authors: Federico Barrios, Federico López, Luis Argerich, Rosa Wachenchauzer
Date: 2016-02-11 00:00:00

This article presents new alternatives to the similarity function for the
TextRank algorithm for automatic summarization of texts. We describe the
generalities of the algorithm and the different functions we propose. Some of
these variants achieve a significative improvement using the same metrics and
dataset as the original publication.
Generating Discriminative Object Proposals via Submodular Ranking
Authors: Yangmuzi Zhang, Zhuolin Jiang, Xi Chen, Larry S. Davis
Date: 2016-02-11 00:00:00
Tasks: Diversity, Image Segmentation, Object, Object Proposal Generation, Segmentation, Semantic Segmentation

A multi-scale greedy-based object proposal generation approach is presented.
Based on the multi-scale nature of objects in images, our approach is built on
top of a hierarchical segmentation. We first identify the representative and
diverse exemplar clusters within each scale by using a diversity ranking
algorithm. Object proposals are obtained by selecting a subset from the
multi-scale segment pool via maximizing a submodular objective function, which
consists of a weighted coverage term, a single-scale diversity term and a
multi-scale reward term. The weighted coverage term forces the selected set of
object proposals to be representative and compact; the single-scale diversity
term encourages choosing segments from different exemplar clusters so that they
will cover as many object patterns as possible; the multi-scale reward term
encourages the selected proposals to be discriminative and selected from
multiple layers generated by the hierarchical image segmentation. The
experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012
segmentation dataset demonstrate the accuracy and efficiency of our object
proposal model. Additionally, we validate our object proposals in simultaneous
segmentation and detection and outperform the state-of-art performance.
Disaggregation of SMAP L3 Brightness Temperatures to 9km using Kernel Machines
Authors: Subit Chakrabarti, Tara Bongiovanni, Jasmeet Judge, Anand Rangarajan, Sanjay Ranka
Date: 2016-01-20 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this study, a machine learning algorithm is used for disaggregation of
SMAP brightness temperatures (T$_{\textrm{B}}$) from 36km to 9km. It uses image
segmentation to cluster the study region based on meteorological and land cover
similarity, followed by a support vector machine based regression that computes
the value of the disaggregated T$_{\textrm{B}}$ at all pixels. High resolution
remote sensing products such as land surface temperature, normalized difference
vegetation index, enhanced vegetation index, precipitation, soil texture, and
land-cover were used for disaggregation. The algorithm was implemented in Iowa,
United States, from April to July 2015, and compared with the SMAP L3_SM_AP
T$_{\textrm{B}}$ product at 9km. It was found that the disaggregated
T$_{\textrm{B}}$ were very similar to the SMAP-T$_{\textrm{B}}$ product, even
for vegetated areas with a mean difference $\leq$ 5K. However, the standard
deviation of the disaggregation was lower by 7K than that of the AP product.
The probability density functions of the disaggregated T$_{\textrm{B}}$ were
similar to the SMAP-T$_{\textrm{B}}$. The results indicate that this algorithm
may be used for disaggregating T$_{\textrm{B}}$ using complex non-linear
correlations on a grid.
Acceleration of the PDHGM on strongly convex subspaces
Authors: Tuomo Valkonen, Thomas Pock
Date: 2015-11-20 00:00:00
Tasks: Deblurring, Denoising

We propose several variants of the primal-dual method due to Chambolle and
Pock. Without requiring full strong convexity of the objective functions, our
methods are accelerated on subspaces with strong convexity. This yields mixed
rates, $O(1/N^2)$ with respect to initialisation and $O(1/N)$ with respect to
the dual sequence, and the residual part of the primal sequence. We demonstrate
the efficacy of the proposed methods on image processing problems lacking
strong convexity, such as total generalised variation denoising and total
variation deblurring.
A Neural Network Anomaly Detector Using the Random Cluster Model
Authors: Robert A. Murphy
Date: 2015-01-28 00:00:00
Tasks: Anomaly Detection, General Classification, regression

The random cluster model is used to define an upper bound on a distance
measure as a function of the number of data points to be classified and the
expected value of the number of classes to form in a hybrid K-means and
regression classification methodology, with the intent of detecting anomalies.
Conditions are given for the identification of classes which contain anomalies
and individual anomalies within identified classes. A neural network model
describes the decision region-separating surface for offline storage and recall
in any new anomaly detection.
Estimating the Mean Number of K-Means Clusters to Form
Authors: Robert A. Murphy
Date: 2015-03-07 00:00:00
Tasks: Form, General Classification

Utilizing the sample size of a dataset, the random cluster model is employed
in order to derive an estimate of the mean number of K-Means clusters to form
during classification of a dataset.
Learning Privately from Multiparty Data
Authors: Jihun Hamm, Paul Cao, Mikhail Belkin
Date: 2016-02-10 00:00:00
Tasks: Activity Recognition, Intrusion Detection, Network Intrusion Detection

Learning a classifier from private data collected by multiple parties is an
important problem that has many potential applications. How can we build an
accurate and differentially private global classifier by combining
locally-trained classifiers from different parties, without access to any
party's private data? We propose to transfer the `knowledge' of the local
classifier ensemble by first creating labeled data from auxiliary unlabeled
data, and then train a global $\epsilon$-differentially private classifier. We
show that majority voting is too sensitive and therefore propose a new risk
weighted by class probabilities estimated from the ensemble. Relative to a
non-private solution, our private solution has a generalization error bounded
by $O(\epsilon^{-2}M^{-2})$ where $M$ is the number of parties. This allows
strong privacy without performance loss when $M$ is large, such as in
crowdsensing applications. We demonstrate the performance of our method with
realistic tasks of activity recognition, network intrusion detection, and
malicious URL detection.
Knowledge Transfer with Medical Language Embeddings
Authors: Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
Date: 2016-02-10 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Language Modelling, Transfer Learning

Identifying relationships between concepts is a key aspect of scientific
knowledge synthesis. Finding these links often requires a researcher to
laboriously search through scien- tific papers and databases, as the size of
these resources grows ever larger. In this paper we describe how distributional
semantics can be used to unify structured knowledge graphs with unstructured
text to predict new relationships between medical concepts, using a
probabilistic generative model. Our approach is also designed to ameliorate
data sparsity and scarcity issues in the medical domain, which make language
modelling more challenging. Specifically, we integrate the medical relational
database (SemMedDB) with text from electronic health records (EHRs) to perform
knowledge graph completion. We further demonstrate the ability of our model to
predict relationships between tokens not appearing in the relational database.
Autoencoding beyond pixels using a learned similarity metric
Authors: Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther
Date: 2015-12-31 00:00:00
Tasks: Generative Adversarial Network, Translation

We present an autoencoder that leverages learned representations to better
measure similarities in data space. By combining a variational autoencoder with
a generative adversarial network we can use learned feature representations in
the GAN discriminator as basis for the VAE reconstruction objective. Thereby,
we replace element-wise errors with feature-wise errors to better capture the
data distribution while offering invariance towards e.g. translation. We apply
our method to images of faces and show that it outperforms VAEs with
element-wise similarity measures in terms of visual fidelity. Moreover, we show
that the method learns an embedding in which high-level abstract visual
features (e.g. wearing glasses) can be modified using simple arithmetic.
Research Priorities for Robust and Beneficial Artificial Intelligence
Authors: Stuart Russell, Daniel Dewey, Max Tegmark
Date: 2016-02-10 00:00:00

Success in the quest for artificial intelligence has the potential to bring
unprecedented benefits to humanity, and it is therefore worthwhile to
investigate how to maximize these benefits while avoiding potential pitfalls.
This article gives numerous examples (which should by no means be construed as
an exhaustive list) of such worthwhile research aimed at ensuring that AI
remains robust and beneficial.
Learning Distributed Representations of Sentences from Unlabelled Data
Authors: Felix Hill, Kyunghyun Cho, Anna Korhonen
Date: 2016-02-10 00:00:00
Proceeding: NAACL 2016 6
Tasks: Representation Learning, Sentence

Unsupervised methods for learning distributed representations of words are
ubiquitous in today's NLP research, but far less is known about the best ways
to learn distributed phrase or sentence representations from unlabelled data.
This paper is a systematic comparison of models that learn such
representations. We find that the optimal approach depends critically on the
intended application. Deeper, more complex models are preferable for
representations to be used in supervised systems, but shallow log-linear models
work best for building representation spaces that can be decoded with simple
spatial distance metrics. We also propose two new unsupervised
representation-learning objectives designed to optimise the trade-off between
training time, domain portability and performance.
Super-Resolved Retinal Image Mosaicing
Authors: Thomas Köhler, Axel Heinrich, Andreas Maier, Joachim Hornegger, Ralf P. Tornow
Date: 2016-02-10 00:00:00

The acquisition of high-resolution retinal fundus images with a large field
of view (FOV) is challenging due to technological, physiological and economic
reasons. This paper proposes a fully automatic framework to reconstruct retinal
images of high spatial resolution and increased FOV from multiple
low-resolution images captured with non-mydriatic, mobile and video-capable but
low-cost cameras. Within the scope of one examination, we scan different
regions on the retina by exploiting eye motion conducted by a patient guidance.
Appropriate views for our mosaicing method are selected based on optic disk
tracking to trace eye movements. For each view, one super-resolved image is
reconstructed by fusion of multiple video frames. Finally, all super-resolved
views are registered to a common reference using a novel polynomial
registration scheme and combined by means of image mosaicing. We evaluated our
framework for a mobile and low-cost video fundus camera. In our experiments, we
reconstructed retinal images of up to 30{\deg} FOV from 10 complementary views
of 15{\deg} FOV. An evaluation of the mosaics by human experts as well as a
quantitative comparison to conventional color fundus images encourage the
clinical usability of our framework.
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Authors: Lionel Pigou, Aäron van den Oord, Sander Dieleman, Mieke Van Herreweghe, Joni Dambre
Date: 2015-06-05 00:00:00
Tasks: Gesture Recognition, Image Captioning, Machine Translation, speech-recognition, Speech Recognition, Translation, Video Classification

Recent studies have demonstrated the power of recurrent neural networks for
machine translation, image captioning and speech recognition. For the task of
capturing temporal structure in video, however, there still remain numerous
open research questions. Current research suggests using a simple temporal
feature pooling strategy to take into account the temporal aspect of video. We
demonstrate that this method is not sufficient for gesture recognition, where
temporal information is more discriminative compared to general video
classification tasks. We explore deep architectures for gesture recognition in
video and propose a new end-to-end trainable neural network architecture
incorporating temporal convolutions and bidirectional recurrence. Our main
contributions are twofold; first, we show that recurrence is crucial for this
task; second, we show that adding temporal convolutions leads to significant
improvements. We evaluate the different approaches on the Montalbano gesture
recognition dataset, where we achieve state-of-the-art results.
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns
Authors: Roel Bertens, Jilles Vreeken, Arno Siebes
Date: 2015-12-22 00:00:00

We study how to obtain concise descriptions of discrete multivariate
sequential data. In particular, how to do so in terms of rich multivariate
sequential patterns that can capture potentially highly interesting
(cor)relations between sequences. To this end we allow our pattern language to
span over the domains (alphabets) of all sequences, allow patterns to overlap
temporally, as well as allow for gaps in their occurrences.
  We formalise our goal by the Minimum Description Length principle, by which
our objective is to discover the set of patterns that provides the most
succinct description of the data. To discover high-quality pattern sets
directly from data, we introduce DITTO, a highly efficient algorithm that
approximates the ideal result very well.
  Experiments show that DITTO correctly discovers the patterns planted in
synthetic data. Moreover, it scales favourably with the length of the data, the
number of attributes, the alphabet sizes. On real data, ranging from sensor
networks to annotated text, DITTO discovers easily interpretable summaries that
provide clear insight in both the univariate and multivariate structure.
Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences
Authors: Roel Bertens, Jilles Vreeken, Arno Siebes
Date: 2015-12-22 00:00:00

Our world is filled with both beautiful and brainy people, but how often does
a Nobel Prize winner also wins a beauty pageant? Let us assume that someone who
is both very beautiful and very smart is more rare than what we would expect
from the combination of the number of beautiful and brainy people. Of course
there will still always be some individuals that defy this stereotype; these
beautiful brainy people are exactly the class of anomaly we focus on in this
paper. They do not posses intrinsically rare qualities, it is the unexpected
combination of factors that makes them stand out.
  In this paper we define the above described class of anomaly and propose a
method to quickly identify them in transaction data. Further, as we take a
pattern set based approach, our method readily explains why a transaction is
anomalous. The effectiveness of our method is thoroughly verified with a wide
range of experiments on both real world and synthetic data.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Authors: Hoo-chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M. Summers
Date: 2016-02-10 00:00:00
Tasks: General Classification, image-classification, Image Classification, Medical Image Classification, Transfer Learning

Remarkable progress has been made in image recognition, primarily due to the
availability of large-scale annotated datasets and the revival of deep CNN.
CNNs enable learning data-driven, highly representative, layered hierarchical
image features from sufficient training data. However, obtaining datasets as
comprehensively annotated as ImageNet in the medical imaging domain remains a
challenge. There are currently three major techniques that successfully employ
CNNs to medical image classification: training the CNN from scratch, using
off-the-shelf pre-trained CNN features, and conducting unsupervised CNN
pre-training with supervised fine-tuning. Another effective method is transfer
learning, i.e., fine-tuning CNN models pre-trained from natural image dataset
to medical image tasks. In this paper, we exploit three important, but
previously understudied factors of employing deep convolutional neural networks
to computer-aided detection problems. We first explore and evaluate different
CNN architectures. The studied models contain 5 thousand to 160 million
parameters, and vary in numbers of layers. We then evaluate the influence of
dataset scale and spatial image context on performance. Finally, we examine
when and why transfer learning from pre-trained ImageNet (via fine-tuning) can
be useful. We study two specific computer-aided detection (CADe) problems,
namely thoraco-abdominal lymph node (LN) detection and interstitial lung
disease (ILD) classification. We achieve the state-of-the-art performance on
the mediastinal LN detection, with 85% sensitivity at 3 false positive per
patient, and report the first five-fold cross-validation classification results
on predicting axial CT slices with ILD categories. Our extensive empirical
evaluation, CNN model analysis and valuable insights can be extended to the
design of high performance CAD systems for other medical imaging tasks.
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines
Authors: Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike Von Luxburg
Date: 2015-06-02 00:00:00

Peer grading is the process of students reviewing each others' work, such as
homework submissions, and has lately become a popular mechanism used in massive
open online courses (MOOCs). Intrigued by this idea, we used it in a course on
algorithms and data structures at the University of Hamburg. Throughout the
whole semester, students repeatedly handed in submissions to exercises, which
were then evaluated both by teaching assistants and by a peer grading
mechanism, yielding a large dataset of teacher and peer grades. We applied
different statistical and machine learning methods to aggregate the peer grades
in order to come up with accurate final grades for the submissions (supervised
and unsupervised, methods based on numeric scores and ordinal rankings).
Surprisingly, none of them improves over the baseline of using the mean peer
grade as the final grade. We discuss a number of possible explanations for
these results and present a thorough analysis of the generated dataset.