pwc-archive/papers-with-abstracts huggingface dataset:
Cultural Diffusion and Trends in Facebook Photographs
Authors: Quanzeng You, Darío García-García, Mahohar Paluri, Jiebo Luo, Jungseock Joo
Date: 2017-05-24 00:00:00

Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.
Logic Tensor Networks for Semantic Image Interpretation
Authors: Ivan Donadello, Luciano Serafini, Artur d'Avila Garcez
Date: 2017-05-24 00:00:00
Tasks: Relational Reasoning, Tensor Networks

Semantic Image Interpretation (SII) is the task of extracting structured
semantic descriptions from images. It is widely agreed that the combined use of
visual data and background knowledge is of great importance for SII. Recently,
Statistical Relational Learning (SRL) approaches have been developed for
reasoning under uncertainty and learning in the presence of data and rich
knowledge. Logic Tensor Networks (LTNs) are an SRL framework which integrates
neural networks with first-order fuzzy logic to allow (i) efficient learning
from noisy data in the presence of logical constraints, and (ii) reasoning with
logical formulas describing general properties of the data. In this paper, we
develop and apply LTNs to two of the main tasks of SII, namely, the
classification of an image's bounding boxes and the detection of the relevant
part-of relations between objects. To the best of our knowledge, this is the
first successful application of SRL to such SII tasks. The proposed approach is
evaluated on a standard image processing benchmark. Experiments show that the
use of background knowledge in the form of logical constraints can improve the
performance of purely data-driven approaches, including the state-of-the-art
Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show
that the use of logical background knowledge adds robustness to the learning
system when errors are present in the labels of the training data.
Efficient, Safe, and Probably Approximately Complete Learning of Action Models
Authors: Roni Stern, Brendan Juba
Date: 2017-05-24 00:00:00

In this paper we explore the theoretical boundaries of planning in a setting
where no model of the agent's actions is given. Instead of an action model, a
set of successfully executed plans are given and the task is to generate a plan
that is safe, i.e., guaranteed to achieve the goal without failing. To this
end, we show how to learn a conservative model of the world in which actions
are guaranteed to be applicable. This conservative model is then given to an
off-the-shelf classical planner, resulting in a plan that is guaranteed to
achieve the goal. However, this reduction from a model-free planning to a
model-based planning is not complete: in some cases a plan will not be found
even when such exists. We analyze the relation between the number of observed
plans and the likelihood that our conservative approach will indeed fail to
solve a solvable problem. Our analysis show that the number of trajectories
needed scales gracefully.
Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing
Authors: Yu-Chuan Su, Kristen Grauman
Date: 2017-03-01 00:00:00
Tasks: Navigate

360$^{\circ}$ video requires human viewers to actively control "where" to
look while watching the video. Although it provides a more immersive experience
of the visual content, it also introduces additional burden for viewers;
awkward interfaces to navigate the video lead to suboptimal viewing
experiences. Virtual cinematography is an appealing direction to remedy these
problems, but conventional methods are limited to virtual environments or rely
on hand-crafted heuristics. We propose a new algorithm for virtual
cinematography that automatically controls a virtual camera within a
360$^{\circ}$ video. Compared to the state of the art, our algorithm allows
more general camera control, avoids redundant outputs, and extracts its output
videos substantially more efficiently. Experimental results on over 7 hours of
real "in the wild" video show that our generalized camera control is crucial
for viewing 360$^{\circ}$ video, while the proposed efficient algorithm is
essential for making the generalized control computationally tractable.
Joint PoS Tagging and Stemming for Agglutinative Languages
Authors: Necva Bölücü, Burcu Can
Date: 2017-05-24 00:00:00
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

The number of word forms in agglutinative languages is theoretically infinite
and this variety in word forms introduces sparsity in many natural language
processing tasks. Part-of-speech tagging (PoS tagging) is one of these tasks
that often suffers from sparsity. In this paper, we present an unsupervised
Bayesian model using Hidden Markov Models (HMMs) for joint PoS tagging and
stemming for agglutinative languages. We use stemming to reduce sparsity in PoS
tagging. Two tasks are jointly performed to provide a mutual benefit in both
tasks. Our results show that joint POS tagging and stemming improves PoS
tagging scores. We present results for Turkish and Finnish as agglutinative
languages and English as a morphologically poor language.
Shape DNA: Basic Generating Functions for Geometric Moment Invariants
Authors: Erbo Li, Yazhou Huang, Dong Xu, Hua Li
Date: 2017-03-07 00:00:00
Tasks: Information Retrieval, Retrieval

Geometric moment invariants (GMIs) have been widely used as basic tool in
shape analysis and information retrieval. Their structure and characteristics
determine efficiency and effectiveness. Two fundamental building blocks or
generating functions (GFs) for invariants are discovered, which are dot product
and vector product of point vectors in Euclidean space. The primitive
invariants (PIs) can be derived by carefully selecting different products of
GFs and calculating the corresponding multiple integrals, which translates
polynomials of coordinates of point vectors into geometric moments. Then the
invariants themselves are expressed in the form of product of moments. This
procedure is just like DNA encoding proteins. All GMIs available in the
literature can be decomposed into linear combinations of PIs. This paper shows
that Hu's seven well known GMIs in computer vision have a more deep structure,
which can be further divided into combination of simpler PIs. In practical
uses, low order independent GMIs are of particular interest. In this paper, a
set of PIs for similarity transformation and affine transformation in 2D are
presented, which are simpler to use, and some of which are newly reported. The
discovery of the two generating functions provides a new perspective of better
understanding shapes in 2D and 3D Euclidean spaces, and the method proposed can
be further extended to higher dimensional spaces and different manifolds, such
as curves, surfaces and so on.
Optical Mapping Near-eye Three-dimensional Display with Correct Focus Cues
Authors: Wei Cui, Liang Gao
Date: 2017-05-24 00:00:00

We present an optical mapping near-eye (OMNI) three-dimensional display
method for wearable devices. By dividing a display screen into different
sub-panels and optically mapping them to various depths, we create a multiplane
volumetric image with correct focus cues for depth perception. The resultant
system can drive the eye's accommodation to the distance that is consistent
with binocular stereopsis, thereby alleviating the vergence-accommodation
conflict, the primary cause for eye fatigue and discomfort. Compared with the
previous methods, the OMNI display offers prominent advantages in adaptability,
image dynamic range, and refresh rate.
Isomorphism between Differential and Moment Invariants under Affine Transform
Authors: Erbo Li, Hua Li
Date: 2017-05-20 00:00:00

The invariant is one of central topics in science, technology and
engineering. The differential invariant is essential in understanding or
describing some important phenomena or procedures in mathematics, physics,
chemistry, biology or computer science etc. The derivation of differential
invariants is usually difficult or complicated. This paper reports a discovery
that under the affine transform, differential invariants have similar
structures with moment invariants up to a scalar function of transform
parameters. If moment invariants are known, relative differential invariants
can be obtained by the substitution of moments by derivatives with the same
order. Whereas moment invariants can be calculated by multiple integrals, this
method provides a simple way to derive differential invariants without the need
to resolve any equation system. Since the definition of moments on different
manifolds or in different dimension of spaces is well established, differential
invariants on or in them will also be well defined. Considering that moments
have a strong background in mathematics and physics, this technique offers a
new view angle to the inner structure of invariants. Projective differential
invariants can also be found in this way with a screening process.
Proximity Variational Inference
Authors: Jaan Altosaar, Rajesh Ranganath, David M. Blei
Date: 2017-05-24 00:00:00
Tasks: Variational Inference

Variational inference is a powerful approach for approximate posterior
inference. However, it is sensitive to initialization and can be subject to
poor local optima. In this paper, we develop proximity variational inference
(PVI). PVI is a new method for optimizing the variational objective that
constrains subsequent iterates of the variational parameters to robustify the
optimization path. Consequently, PVI is less sensitive to initialization and
optimization quirks and finds better local optima. We demonstrate our method on
three proximity statistics. We study PVI on a Bernoulli factor model and
sigmoid belief network with both real and synthetic data and compare to
deterministic annealing (Katahira et al., 2008). We highlight the flexibility
of PVI by designing a proximity statistic for Bayesian deep learning models
such as the variational autoencoder (Kingma and Welling, 2014; Rezende et al.,
2014). Empirically, we show that PVI consistently finds better local optima and
gives better predictive performance.
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis
Authors: Yuandong Tian
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8

In this paper, we explore theoretical properties of training a two-layered
ReLU network $g(\mathbf{x}; \mathbf{w}) = \sum_{j=1}^K
\sigma(\mathbf{w}_j^T\mathbf{x})$ with centered $d$-dimensional spherical
Gaussian input $\mathbf{x}$ ($\sigma$=ReLU). We train our network with gradient
descent on $\mathbf{w}$ to mimic the output of a teacher network with the same
architecture and fixed parameters $\mathbf{w}^*$. We show that its population
gradient has an analytical formula, leading to interesting theoretical analysis
of critical points and convergence behaviors. First, we prove that critical
points outside the hyperplane spanned by the teacher parameters
("out-of-plane") are not isolated and form manifolds, and characterize in-plane
critical-point-free regions for two ReLU case. On the other hand, convergence
to $\mathbf{w}^*$ for one ReLU node is guaranteed with at least
$(1-\epsilon)/2$ probability, if weights are initialized randomly with standard
deviation upper-bounded by $O(\epsilon/\sqrt{d})$, consistent with empirical
practice. For network with many ReLU nodes, we prove that an infinitesimal
perturbation of weight initialization results in convergence towards
$\mathbf{w}^*$ (or its permutation), a phenomenon known as spontaneous
symmetric-breaking (SSB) in physics. We assume no independence of ReLU
activations. Simulation verifies our findings.
Attention-based Natural Language Person Retrieval
Authors: Tao Zhou, Muhao Chen, Jie Yu, Demetri Terzopoulos
Date: 2017-05-24 00:00:00
Tasks: image-classification, Image Classification, Person Retrieval, Region Proposal, Retrieval

Following the recent progress in image classification and captioning using
deep learning, we develop a novel natural language person retrieval system
based on an attention mechanism. More specifically, given the description of a
person, the goal is to localize the person in an image. To this end, we first
construct a benchmark dataset for natural language person retrieval. To do so,
we generate bounding boxes for persons in a public image dataset from the
segmentation masks, which are then annotated with descriptions and attributes
using the Amazon Mechanical Turk. We then adopt a region proposal network in
Faster R-CNN as a candidate region generator. The cropped images based on the
region proposals as well as the whole images with attention weights are fed
into Convolutional Neural Networks for visual feature extraction, while the
natural language expression and attributes are input to Bidirectional Long
Short- Term Memory (BLSTM) models for text feature extraction. The visual and
text features are integrated to score region proposals, and the one with the
highest score is retrieved as the output of our system. The experimental
results show significant improvement over the state-of-the-art method for
generic object retrieval and this line of research promises to benefit search
in surveillance video footage.
Unsupervised Learning Layers for Video Analysis
Authors: Liang Zhao, Yang Wang, Yi Yang, Wei Xu
Date: 2017-05-24 00:00:00
Tasks: Object Localization

This paper presents two unsupervised learning layers (UL layers) for
label-free video analysis: one for fully connected layers, and the other for
convolutional ones. The proposed UL layers can play two roles: they can be the
cost function layer for providing global training signal; meanwhile they can be
added to any regular neural network layers for providing local training signals
and combined with the training signals backpropagated from upper layers for
extracting both slow and fast changing features at layers of different depths.
Therefore, the UL layers can be used in either pure unsupervised or
semi-supervised settings. Both a closed-form solution and an online learning
algorithm for two UL layers are provided. Experiments with unlabeled synthetic
and real-world videos demonstrated that the neural networks equipped with UL
layers and trained with the proposed online learning algorithm can extract
shape and motion information from video sequences of moving objects. The
experiments demonstrated the potential applications of UL layers and online
learning algorithm to head orientation estimation and moving object
localization.
Whitening-Free Least-Squares Non-Gaussian Component Analysis
Authors: Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
Date: 2016-03-03 00:00:00
Tasks: Dimensionality Reduction

Non-Gaussian component analysis (NGCA) is an unsupervised linear dimension
reduction method that extracts low-dimensional non-Gaussian "signals" from
high-dimensional data contaminated with Gaussian noise. NGCA can be regarded as
a generalization of projection pursuit (PP) and independent component analysis
(ICA) to multi-dimensional and dependent non-Gaussian components. Indeed,
seminal approaches to NGCA are based on PP and ICA. Recently, a novel NGCA
approach called least-squares NGCA (LSNGCA) has been developed, which gives a
solution analytically through least-squares estimation of log-density gradients
and eigendecomposition. However, since pre-whitening of data is involved in
LSNGCA, it performs unreliably when the data covariance matrix is
ill-conditioned, which is often the case in high-dimensional data analysis. In
this paper, we propose a whitening-free LSNGCA method and experimentally
demonstrate its superiority.
Anti-spoofing Methods for Automatic SpeakerVerification System
Authors: Galina Lavrentyeva, Sergey Novoselov, Konstantin Simonchik
Date: 2017-05-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speaker Verification, speech-recognition, Speech Recognition

Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.
Local Group Invariant Representations via Orbit Embeddings
Authors: Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher, Bernhard Schölkopf
Date: 2016-12-06 00:00:00
Tasks: Rotated MNIST

Invariance to nuisance transformations is one of the desirable properties of
effective representations. We consider transformations that form a \emph{group}
and propose an approach based on kernel methods to derive local group invariant
representations. Locality is achieved by defining a suitable probability
distribution over the group which in turn induces distributions in the input
feature space. We learn a decision function over these distributions by
appealing to the powerful framework of kernel methods and generate local
invariant random feature maps via kernel approximations. We show uniform
convergence bounds for kernel approximation and provide excess risk bounds for
learning with these features. We evaluate our method on three real datasets,
including Rotated MNIST and CIFAR-10, and observe that it outperforms competing
kernel based approaches. The proposed method also outperforms deep CNN on
Rotated-MNIST and performs comparably to the recently proposed
group-equivariant CNN.
Audio-replay attack detection countermeasures
Authors: Galina Lavrentyeva, Sergey Novoselov, Egor Malykh, Alexander Kozlov, Oleg Kudashev, Vadim Shchemelinin
Date: 2017-05-24 00:00:00
Tasks: Deep Learning, Speaker Verification

This paper presents the Speech Technology Center (STC) replay attack
detection systems proposed for Automatic Speaker Verification Spoofing and
Countermeasures Challenge 2017. In this study we focused on comparison of
different spoofing detection approaches. These were GMM based methods, high
level features extraction with simple classifier and deep learning frameworks.
Experiments performed on the development and evaluation parts of the challenge
dataset demonstrated stable efficiency of deep learning approaches in case of
changing acoustic conditions. At the same time SVM classifier with high level
features provided a substantial input in the efficiency of the resulting STC
systems according to the fusion systems results.
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
Authors: Rodrigo Toro Icarte, Jorge A. Baier, Cristian Ruz, Alvaro Soto
Date: 2017-05-24 00:00:00
Tasks: Image Retrieval, Retrieval, Sentence, Visual Reasoning

The knowledge representation community has built general-purpose ontologies
which contain large amounts of commonsense knowledge over relevant aspects of
the world, including useful visual information, e.g.: "a ball is used by a
football player", "a tennis player is located at a tennis court". Current
state-of-the-art approaches for visual recognition do not exploit these
rule-based knowledge sources. Instead, they learn recognition models directly
from training examples. In this paper, we study how general-purpose
ontologies---specifically, MIT's ConceptNet ontology---can improve the
performance of state-of-the-art vision systems. As a testbed, we tackle the
problem of sentence-based image retrieval. Our retrieval approach incorporates
knowledge from ConceptNet on top of a large pool of object detectors derived
from a deep learning technique. In our experiments, we show that ConceptNet can
improve performance on a common benchmark dataset. Key to our performance is
the use of the ESPGAME dataset to select visually relevant relations from
ConceptNet. Consequently, a main conclusion of this work is that
general-purpose commonsense ontologies improve performance on visual reasoning
tasks when properly filtered to select meaningful visual relations.
Parsing with CYK over Distributed Representations
Authors: Fabio Massimo Zanzotto, Giordano Cristini, Giorgio Satta
Date: 2017-05-24 00:00:00

Syntactic parsing is a key task in natural language processing. This task has
been dominated by symbolic, grammar-based parsers. Neural networks, with their
distributed representations, are challenging these methods. In this article we
show that existing symbolic parsing algorithms can cross the border and be
entirely formulated over distributed representations. To this end we introduce
a version of the traditional Cocke-Younger-Kasami (CYK) algorithm, called
D-CYK, which is entirely defined over distributed representations. Our D-CYK
uses matrix multiplication on real number matrices of size independent of the
length of the input string. These operations are compatible with traditional
neural networks. Experiments show that our D-CYK approximates the original CYK
algorithm. By showing that CYK can be entirely performed on distributed
representations, we open the way to the definition of recurrent layers of
CYK-informed neural networks.