pwc-archive/papers-with-abstracts huggingface dataset:
Gauging Variational Inference
Authors: Sungsoo Ahn, Michael Chertkov, Jinwoo Shin
Date: 2017-03-03 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Variational Inference

Computing partition function is the most important statistical inference task
arising in applications of Graphical Models (GM). Since it is computationally
intractable, approximate methods have been used to resolve the issue in
practice, where mean-field (MF) and belief propagation (BP) are arguably the
most popular and successful approaches of a variational type. In this paper, we
propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP
(G-BP), improving MF and BP, respectively. Both provide lower bounds for the
partition function by utilizing the so-called gauge transformation which
modifies factors of GM while keeping the partition function invariant.
Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop
of a special structure, even though the bare MF and BP perform badly in this
case. Our extensive experiments, on complete GMs of relatively small size and
on large GM (up-to 300 variables) confirm that the newly proposed algorithms
outperform and generalize MF and BP.
Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier
Authors: Zhiming Wang, Xiaolong Li, Jun Zhou
Date: 2017-09-12 00:00:00
Tasks: Decision Making, Keyword Spotting, Small-Footprint Keyword Spotting, speech-recognition, Speech Recognition

Mainly for the sake of solving the lack of keyword-specific data, we propose
one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and
Connectionist Temporal Classifier (CTC) on power-constrained small-footprint
mobile devices, taking full advantage of general corpus from continuous speech
recognition which is of great amount. DNN is to directly predict the posterior
of phoneme units of any personally customized key-phrase, and CTC to produce a
confidence score of the given phoneme sequence as responsive decision-making
mechanism. The CTC-KWS has competitive performance in comparison with purely
DNN based keyword specific KWS, but not increasing any computational
complexity.
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Authors: Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin
Date: 2017-09-12 00:00:00
Tasks: Clustering, Graph Embedding, Graph Learning, Graph Mining, Node Clustering

Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application.
Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering
Authors: Nan Xu, Yanqing Guo, Jiujun Wang, Xiangyang Luo, Ran He
Date: 2017-09-12 00:00:00
Tasks: Clustering, Metric Learning, MULTI-VIEW LEARNING, Multi-view Subspace Clustering

Due to the existence of various views or representations in many real-world
data, multi-view learning has drawn much attention recently. Multi-view
spectral clustering methods based on similarity matrixes or graphs are pretty
popular. Generally, these algorithms learn informative graphs by directly
utilizing original data. However, in the real-world applications, original data
often contain noises and outliers that lead to unreliable graphs. In addition,
different views may have different contributions to data clustering. In this
paper, a novel Multiview Subspace Clustering method unifying Adaptive
neighbours and Metric learning (MSCAM), is proposed to address the above
problems. In this method, we use the subspace representations of different
views to adaptively learn a consensus similarity matrix, uncovering the
subspace structure and avoiding noisy nature of original data. For all views,
we also learn different Mahalanobis matrixes that parameterize the squared
distances and consider the contributions of different views. Further, we
constrain the graph constructed by the similarity matrix to have exact c (c is
the number of clusters) connected components. An iterative algorithm is
developed to solve this optimization problem. Moreover, experiments on a
synthetic dataset and different real-world datasets demonstrate the
effectiveness of MSCAM.
Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure
Authors: Tao Yang, Paul Thompson, Sihai Zhao, Jieping Ye
Date: 2017-09-12 00:00:00
Tasks: Variable Selection

Genome-wide association studies (GWA studies or GWAS) investigate the
relationships between genetic variants such as single-nucleotide polymorphisms
(SNPs) and individual traits. Recently, incorporating biological priors
together with machine learning methods in GWA studies has attracted increasing
attention. However, in real-world, nucleotide-level bio-priors have not been
well-studied to date. Alternatively, studies at gene-level, for example,
protein--protein interactions and pathways, are more rigorous and legitimate,
and it is potentially beneficial to utilize such gene-level priors in GWAS. In
this paper, we proposed a novel two-level structured sparse model, called
Sparse Group Lasso with Group-level Graph structure (SGLGG), for GWAS. It can
be considered as a sparse group Lasso along with a group-level graph Lasso.
Essentially, SGLGG penalizes the nucleotide-level sparsity as well as takes
advantages of gene-level priors (both gene groups and networks), to identifying
phenotype-associated risk SNPs. We employ the alternating direction method of
multipliers algorithm to optimize the proposed model. Our experiments on the
Alzheimer's Disease Neuroimaging Initiative whole genome sequence data and
neuroimage data demonstrate the effectiveness of SGLGG. As a regression model,
it is competitive to the state-of-the-arts sparse models; as a variable
selection method, SGLGG is promising for identifying Alzheimer's
disease-related risk SNPs.
What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music
Authors: Carlos Cancino-Chacón, Maarten Grachten, David R. W. Sears, Gerhard Widmer
Date: 2017-09-11 00:00:00

In this paper we present preliminary work examining the relationship between
the formation of expectations and the realization of musical performances,
paying particular attention to expressive tempo and dynamics. To compute
features that reflect what a listener is expecting to hear, we employ a
computational model of auditory expectation called the Information Dynamics of
Music model (IDyOM). We then explore how well these expectancy features -- when
combined with score descriptors using the Basis-Function modeling approach --
can predict expressive tempo and dynamics in a dataset of Mozart piano sonata
performances. Our results suggest that using expectancy features significantly
improves the predictions for tempo.
Budgeted Experiment Design for Causal Structure Learning
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
Date: 2017-09-11 00:00:00
Proceeding: ICML 2018 7

We study the problem of causal structure learning when the experimenter is
limited to perform at most $k$ non-adaptive experiments of size $1$. We
formulate the problem of finding the best intervention target set as an
optimization problem, which aims to maximize the average number of edges whose
directions are resolved. We prove that the corresponding objective function is
submodular and a greedy algorithm suffices to achieve
$(1-\frac{1}{e})$-approximation of the optimal value. We further present an
accelerated variant of the greedy algorithm, which can lead to orders of
magnitude performance speedup. We validate our proposed approach on synthetic
and real graphs. The results show that compared to the purely observational
setting, our algorithm orients the majority of the edges through a considerably
small number of interventions.
Manifold Learning Using Kernel Density Estimation and Local Principal Components Analysis
Authors: Kitty Mohammed, Hariharan Narayanan
Date: 2017-09-11 00:00:00
Tasks: Density Estimation

We consider the problem of recovering a $d-$dimensional manifold $\mathcal{M}
\subset \mathbb{R}^n$ when provided with noiseless samples from $\mathcal{M}$.
There are many algorithms (e.g., Isomap) that are used in practice to fit
manifolds and thus reduce the dimensionality of a given data set. Ideally, the
estimate $\mathcal{M}_\mathrm{put}$ of $\mathcal{M}$ should be an actual
manifold of a certain smoothness; furthermore, $\mathcal{M}_\mathrm{put}$
should be arbitrarily close to $\mathcal{M}$ in Hausdorff distance given a
large enough sample. Generally speaking, existing manifold learning algorithms
do not meet these criteria. Fefferman, Mitter, and Narayanan (2016) have
developed an algorithm whose output is provably a manifold. The key idea is to
define an approximate squared-distance function (asdf) to $\mathcal{M}$. Then,
$\mathcal{M}_\mathrm{put}$ is given by the set of points where the gradient of
the asdf is orthogonal to the subspace spanned by the largest $n - d$
eigenvectors of the Hessian of the asdf. As long as the asdf meets certain
regularity conditions, $\mathcal{M}_\mathrm{put}$ is a manifold that is
arbitrarily close in Hausdorff distance to $\mathcal{M}$. In this paper, we
define two asdfs that can be calculated from the data and show that they meet
the required regularity conditions. The first asdf is based on kernel density
estimation, and the second is based on estimation of tangent spaces using local
principal components analysis.
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Authors: Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Kate Saenko
Date: 2017-04-18 00:00:00
Proceeding: ICCV 2017 10
Tasks: Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

Natural language questions are inherently compositional, and many are most
easily answered by reasoning about their decomposition into modular
sub-problems. For example, to answer "is there an equal number of balls and
boxes?" we can look for balls, look for boxes, count them, and compare the
results. The recently proposed Neural Module Network (NMN) architecture
implements this approach to question answering by parsing questions into
linguistic substructures and assembling question-specific deep networks from
smaller modules that each solve one subtask. However, existing NMN
implementations rely on brittle off-the-shelf parsers, and are restricted to
the module configurations proposed by these parsers rather than learning them
from data. In this paper, we propose End-to-End Module Networks (N2NMNs), which
learn to reason by directly predicting instance-specific network layouts
without the aid of a parser. Our model learns to generate network structures
(by imitating expert demonstrations) while simultaneously learning network
parameters (using the downstream task loss). Experimental results on the new
CLEVR dataset targeted at compositional question answering show that N2NMNs
achieve an error reduction of nearly 50% relative to state-of-the-art
attentional approaches, while discovering interpretable network architectures
specialized for each question.
Holistic, Instance-Level Human Parsing
Authors: Qizhu Li, Anurag Arnab, Philip H. S. Torr
Date: 2017-09-11 00:00:00
Tasks: Human Detection, Human Parsing, Multi-Human Parsing, Object, Segmentation

Object parsing -- the task of decomposing an object into its semantic parts
-- has traditionally been formulated as a category-level segmentation problem.
Consequently, when there are multiple objects in an image, current methods
cannot count the number of objects in the scene, nor can they determine which
part belongs to which object. We address this problem by segmenting the parts
of objects at an instance-level, such that each pixel in the image is assigned
a part label, as well as the identity of the object it belongs to. Moreover, we
show how this approach benefits us in obtaining segmentations at coarser
granularities as well. Our proposed network is trained end-to-end given
detections, and begins with a category-level segmentation module. Thereafter, a
differentiable Conditional Random Field, defined over a variable number of
instances for every input image, reasons about the identity of each part by
associating it with a human detection. In contrast to other approaches, our
method can handle the varying number of people in each image and our holistic
network produces state-of-the-art results in instance-level part and human
segmentation, together with competitive results in category-level part
segmentation, all achieved by a single forward-pass through our neural network.
The impossibility of "fairness": a generalized impossibility result for decisions
Authors: Thomas Miconi
Date: 2017-07-05 00:00:00
Tasks: Fairness, Prediction, Specificity

Various measures can be used to estimate bias or unfairness in a predictor.
Previous work has already established that some of these measures are
incompatible with each other. Here we show that, when groups differ in
prevalence of the predicted event, several intuitive, reasonable measures of
fairness (probability of positive prediction given occurrence or
non-occurrence; probability of occurrence given prediction or non-prediction;
and ratio of predictions over occurrences for each group) are all mutually
exclusive: if one of them is equal among groups, the other two must differ. The
only exceptions are for perfect, or trivial (always-positive or
always-negative) predictors. As a consequence, any non-perfect, non-trivial
predictor must necessarily be "unfair" under two out of three reasonable sets
of criteria. This result readily generalizes to a wide range of well-known
statistical quantities (sensitivity, specificity, false positive rate,
precision, etc.), all of which can be divided into three mutually exclusive
groups. Importantly, The results applies to all predictors, whether algorithmic
or human. We conclude with possible ways to handle this effect when assessing
and designing prediction methods.
Words or Characters? Fine-grained Gating for Reading Comprehension
Authors: Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov
Date: 2016-11-06 00:00:00
Tasks: Question Answering, Reading Comprehension, TAG

Previous work combines word-level and character-level representations using
concatenation or scalar weighting, which is suboptimal for high-level tasks
like reading comprehension. We present a fine-grained gating mechanism to
dynamically combine word-level and character-level representations based on
properties of the words. We also extend the idea of fine-grained gating to
modeling the interaction between questions and paragraphs for reading
comprehension. Experiments show that our approach can improve the performance
on reading comprehension tasks, achieving new state-of-the-art results on the
Children's Book Test dataset. To demonstrate the generality of our gating
mechanism, we also show improved results on a social media tag prediction task.
On the definition of Shape Parts: a Dominant Sets Approach
Authors: Foteini Fotopoulou, George Economou
Date: 2017-09-11 00:00:00
Tasks: graph construction, graph partitioning

In the present paper a novel graph-based approach to the shape decomposition
problem is addressed. The shape is appropriately transformed into a visibility
graph enriched with local neighborhood information. A two-step diffusion
process is then applied to the visibility graph that efficiently enhances the
information provided, thus leading to a more robust and meaningful graph
construction. Inspired by the notion of a clique as a strict cluster
definition, the dominant sets algorithm is invoked, slightly modified to
comport with the specific problem of defining shape parts. The cluster
cohesiveness and a node participation vector are two important outputs of the
proposed graph partitioning method. Opposed to most of the existing techniques,
the final number of the clusters is determined automatically, by estimating the
cluster cohesiveness on a random network generation process. Experimental
results on several shape databases show the effectiveness of our framework for
graph based shape decomposition.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
Authors: Sattar Vakili, Qing Zhao, Chang Liu, Chen-Nee Chuah
Date: 2017-09-11 00:00:00
Tasks: Active Learning, Anomaly Detection, General Classification

We consider the problem of detecting a few targets among a large number of
hierarchical data streams. The data streams are modeled as random processes
with unknown and potentially heavy-tailed distributions. The objective is an
active inference strategy that determines, sequentially, which data stream to
collect samples from in order to minimize the sample complexity under a
reliability constraint. We propose an active inference strategy that induces a
biased random walk on the tree-structured hierarchy based on confidence bounds
of sample statistics. We then establish its order optimality in terms of both
the size of the search space (i.e., the number of data streams) and the
reliability requirement. The results find applications in hierarchical heavy
hitter detection, noisy group testing, and adaptive sampling for active
learning, classification, and stochastic root finding.
False arrhythmia alarm reduction in the intensive care unit
Authors: Andrea S. Li, Alistair E. W. Johnson, Roger G. Mark
Date: 2017-09-11 00:00:00
Tasks: Dynamic Time Warping, Specificity

Research has shown that false alarms constitute more than 80% of the alarms
triggered in the intensive care unit (ICU). The high false arrhythmia alarm
rate has severe implications such as disruption of patient care, caregiver
alarm fatigue, and desensitization from clinical staff to real life-threatening
alarms. A method to reduce the false alarm rate would therefore greatly benefit
patients as well as nurses in their ability to provide care. We here develop
and describe a robust false arrhythmia alarm reduction system for use in the
ICU. Building off of work previously described in the literature, we make use
of signal processing and machine learning techniques to identify true and false
alarms for five arrhythmia types. This baseline algorithm alone is able to
perform remarkably well, with a sensitivity of 0.908, a specificity of 0.838,
and a PhysioNet/CinC challenge score of 0.756. We additionally explore dynamic
time warping techniques on both the entire alarm signal as well as on a
beat-by-beat basis in an effort to improve performance of ventricular
tachycardia, which has in the literature been one of the hardest arrhythmias to
classify. Such an algorithm with strong performance and efficiency could
potentially be translated for use in the ICU to promote overall patient care
and recovery.
Exploring Geometric Property Thresholds For Filtering Non-Text Regions In A Connected Component Based Text Detection Application
Authors: Teresa Nicole Brooks
Date: 2017-09-11 00:00:00
Tasks: Text Detection

Automated text detection is a difficult computer vision task. In order to
accurately detect and identity text in an image or video, two major problems
must be addressed. The primary problem is implementing a robust and reliable
method for distinguishing text vs non-text regions in images and videos. Part
of the difficulty stems from the almost unlimited combinations of fonts,
lighting conditions, distortions, and other variations that can be found in
images and videos. This paper explores key properties of two popular and proven
methods for implementing text detection; maximum stable external regions (MSER)
and stroke width variation.
A parameterized activation function for learning fuzzy logic operations in deep neural networks
Authors: Luke B. Godfrey, Michael S. Gashler
Date: 2017-08-28 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

We present a deep learning architecture for learning fuzzy logic expressions.
Our model uses an innovative, parameterized, differentiable activation function
that can learn a number of logical operations by gradient descent. This
activation function allows a neural network to determine the relationships
between its input variables and provides insight into the logical significance
of learned network parameters. We provide a theoretical basis for this
parameterization and demonstrate its effectiveness and utility by successfully
applying our model to five classification problems from the UCI Machine
Learning Repository.
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition
Authors: Dominic Seyler, Tatiana Dembelova, Luciano del Corro, Johannes Hoffart, Gerhard Weikum
Date: 2017-09-11 00:00:00
Tasks: Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

KnowNER is a multilingual Named Entity Recognition (NER) system that
leverages different degrees of external knowledge. A novel modular framework
divides the knowledge into four categories according to the depth of knowledge
they convey. Each category consists of a set of features automatically
generated from different information sources (such as a knowledge-base, a list
of names or document-specific semantic annotations) and is used to train a
conditional random field (CRF). Since those information sources are usually
multilingual, KnowNER can be easily trained for a wide range of languages. In
this paper, we show that the incorporation of deeper knowledge systematically
boosts accuracy and compare KnowNER with state-of-the-art NER approaches across
three languages (i.e., English, German and Spanish) performing amongst
state-of-the art systems in all of them.