pwc-archive/papers-with-abstracts huggingface dataset:
DeepCas: an End-to-end Predictor of Information Cascades
Authors: Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei
Date: 2016-11-16 00:00:00

Information cascades, effectively facilitated by most social network
platforms, are recognized as a major factor in almost every social success and
disaster in these networks. Can cascades be predicted? While many believe that
they are inherently unpredictable, recent work has shown that some key
properties of information cascades, such as size, growth, and shape, can be
predicted by a machine learning algorithm that combines many features. These
predictors all depend on a bag of hand-crafting features to represent the
cascade network and the global network structure. Such features, always
carefully and sometimes mysteriously designed, are not easy to extend or to
generalize to a different platform or domain.
  Inspired by the recent successes of deep learning in multiple data mining
tasks, we investigate whether an end-to-end deep learning approach could
effectively predict the future size of cascades. Such a method automatically
learns the representation of individual cascade graphs in the context of the
global network structure, without hand-crafted features and heuristics. We find
that node embeddings fall short of predictive power, and it is critical to
learn the representation of a cascade graph as a whole. We present algorithms
that learn the representation of cascade graphs in an end-to-end manner, which
significantly improve the performance of cascade prediction over strong
baselines that include feature based methods, node embedding methods, and graph
kernel methods. Our results also provide interesting implications for cascade
prediction in general.
Fast On-Line Kernel Density Estimation for Active Object Localization
Authors: Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell
Date: 2016-11-16 00:00:00
Tasks: Active Object Localization, Density Estimation, Object Localization

A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper, we propose a novel method for prior learning and
active object localization for this kind of knowledge-driven search in static
images. In our system, prior situation knowledge is captured by a set of
flexible, kernel-based density estimations---a situation model---that represent
the expected spatial structure of the given situation. These estimations are
efficiently updated by information gained as the system searches for relevant
objects, allowing the system to use context as it is discovered to narrow the
search.
  More specifically, at any given time in a run on a test image, our system
uses image features plus contextual information it has discovered to identify a
small subset of training images---an importance cluster---that is deemed most
similar to the given test image, given the context. This subset is used to
generate an updated situation model in an on-line fashion, using an efficient
multipole expansion technique.
  As a proof of concept, we apply our algorithm to a highly varied and
challenging dataset consisting of instances of a "dog-walking" situation. Our
results support the hypothesis that dynamically-rendered, context-based
probability models can support efficient object localization in visual
situations. Moreover, our approach is general enough to be applied to diverse
machine learning paradigms requiring interpretable, probabilistic
representations generated from partially observed data.
Neural Style Representations and the Large-Scale Classification of Artistic Style
Authors: Jeremiah Johnson
Date: 2016-11-16 00:00:00
Tasks: General Classification

The artistic style of a painting is a subtle aesthetic judgment used by art
historians for grouping and classifying artwork. The recently introduced
`neural-style' algorithm substantially succeeds in merging the perceived
artistic style of one image or set of images with the perceived content of
another. In light of this and other recent developments in image analysis via
convolutional neural networks, we investigate the effectiveness of a
`neural-style' representation for classifying the artistic style of paintings.
The Life of Lazarillo de Tormes and of His Machine Learning Adversities
Authors: Javier de la Rosa, Juan-Luis Suárez
Date: 2016-11-16 00:00:00
Tasks: Attribute, BIG-bench Machine Learning

Summit work of the Spanish Golden Age and forefather of the so-called
picaresque novel, The Life of Lazarillo de Tormes and of His Fortunes and
Adversities still remains an anonymous text. Although distinguished scholars
have tried to attribute it to different authors based on a variety of criteria,
a consensus has yet to be reached. The list of candidates is long and not all
of them enjoy the same support within the scholarly community. Analyzing their
works from a data-driven perspective and applying machine learning techniques
for style and text fingerprinting, we shed light on the authorship of the
Lazarillo. As in a state-of-the-art survey, we discuss the methods used and how
they perform in our specific case. According to our methodology, the most
likely author seems to be Juan Arce de Ot\'alora, closely followed by Alfonso
de Vald\'es. The method states that not certain attribution can be made with
the given corpus.
Chi-squared Amplification: Identifying Hidden Hubs
Authors: Ravi Kannan, Santosh Vempala
Date: 2016-08-12 00:00:00

We consider the following general hidden hubs model: an $n \times n$ random
matrix $A$ with a subset $S$ of $k$ special rows (hubs): entries in rows
outside $S$ are generated from the probability distribution $p_0 \sim
N(0,\sigma_0^2)$; for each row in $S$, some $k$ of its entries are generated
from $p_1 \sim N(0,\sigma_1^2)$, $\sigma_1>\sigma_0$, and the rest of the
entries from $p_0$. The problem is to identify the high-degree hubs
efficiently. This model includes and significantly generalizes the planted
Gaussian Submatrix Model, where the special entries are all in a $k \times k$
submatrix. There are two well-known barriers: if $k\geq c\sqrt{n\ln n}$, just
the row sums are sufficient to find $S$ in the general model. For the submatrix
problem, this can be improved by a $\sqrt{\ln n}$ factor to $k \ge c\sqrt{n}$
by spectral methods or combinatorial methods. In the variant with $p_0=\pm 1$
(with probability $1/2$ each) and $p_1\equiv 1$, neither barrier has been
broken.
  We give a polynomial-time algorithm to identify all the hidden hubs with high
probability for $k \ge n^{0.5-\delta}$ for some $\delta >0$, when
$\sigma_1^2>2\sigma_0^2$. The algorithm extends to the setting where planted
entries might have different variances each at least as large as $\sigma_1^2$.
We also show a nearly matching lower bound: for $\sigma_1^2 \le 2\sigma_0^2$,
there is no polynomial-time Statistical Query algorithm for distinguishing
between a matrix whose entries are all from $N(0,\sigma_0^2)$ and a matrix with
$k=n^{0.5-\delta}$ hidden hubs for any $\delta >0$. The lower bound as well as
the algorithm are related to whether the chi-squared distance of the two
distributions diverges. At the critical value $\sigma_1^2=2\sigma_0^2$, we show
that the general hidden hubs problem can be solved for $k\geq c\sqrt n(\ln
n)^{1/4}$, improving on the naive row sum-based method.
Image Credibility Analysis with Effective Domain Transferred Deep Networks
Authors: Zhiwei Jin, Juan Cao, Jiebo Luo, Yongdong Zhang
Date: 2016-11-16 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

Numerous fake images spread on social media today and can severely jeopardize
the credibility of online content to public. In this paper, we employ deep
networks to learn distinct fake image related features. In contrast to
authentic images, fake images tend to be eye-catching and visually striking.
Compared with traditional visual recognition tasks, it is extremely challenging
to understand these psychologically triggered visual patterns in fake images.
Traditional general image classification datasets, such as ImageNet set, are
designed for feature learning at the object level but are not suitable for
learning the hyper-features that would be required by image credibility
analysis. In order to overcome the scarcity of training samples of fake images,
we first construct a large-scale auxiliary dataset indirectly related to this
task. This auxiliary dataset contains 0.6 million weakly-labeled fake and real
images collected automatically from social media. Through an AdaBoost-like
transfer learning algorithm, we train a CNN model with a few instances in the
target training set and 0.6 million images in the collected auxiliary set. This
learning algorithm is able to leverage knowledge from the auxiliary set and
gradually transfer it to the target task. Experiments on a real-world testing
set show that our proposed domain transferred CNN model outperforms several
competing baselines. It obtains superiror results over transfer learning
methods based on the general ImageNet set. Moreover, case studies show that our
proposed method reveals some interesting patterns for distinguishing fake and
authentic images.
Asynchronous Decentralized 20 Questions for Adaptive Search
Authors: Theodoros Tsiligkaridis
Date: 2015-11-10 00:00:00

This paper considers the problem of adaptively searching for an unknown
target using multiple agents connected through a time-varying network topology.
Agents are equipped with sensors capable of fast information processing, and we
propose a decentralized collaborative algorithm for controlling their search
given noisy observations. Specifically, we propose decentralized extensions of
the adaptive query-based search strategy that combines elements from the 20
questions approach and social learning. Under standard assumptions on the
time-varying network dynamics, we prove convergence to correct consensus on the
value of the parameter as the number of iterations go to infinity. The
convergence analysis takes a novel approach using martingale-based techniques
combined with spectral graph theory. Our results establish that stability and
consistency can be maintained even with one-way updating and randomized
pairwise averaging, thus providing a scalable low complexity method with
performance guarantees. We illustrate the effectiveness of our algorithm for
random network topologies.
ASPeRiX, a First Order Forward Chaining Approach for Answer Set Computing
Authors: Claire Lefèvre, Christopher Béatrix, Igor Stéphan, Laurent Garcia
Date: 2015-03-26 00:00:00
Tasks: Negation

The natural way to use Answer Set Programming (ASP) to represent knowledge in
Artificial Intelligence or to solve a combinatorial problem is to elaborate a
first order logic program with default negation. In a preliminary step this
program with variables is translated in an equivalent propositional one by a
first tool: the grounder. Then, the propositional program is given to a second
tool: the solver. This last one computes (if they exist) one or many answer
sets (stable models) of the program, each answer set encoding one solution of
the initial problem. Until today, almost all ASP systems apply this two steps
computation. In this article, the project ASPeRiX is presented as a first order
forward chaining approach for Answer Set Computing. This project was amongst
the first to introduce an approach of answer set computing that escapes the
preliminary phase of rule instantiation by integrating it in the search
process. The methodology applies a forward chaining of first order rules that
are grounded on the fly by means of previously produced atoms. Theoretical
foundations of the approach are presented, the main algorithms of the ASP
solver ASPeRiX are detailed and some experiments and comparisons with existing
systems are provided.
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search
Authors: Tu Bui, Leonardo Ribeiro, Moacir Ponti, John Collomosse
Date: 2016-11-16 00:00:00
Tasks: Data Augmentation, Dimensionality Reduction, General Classification, Image Retrieval, Retrieval, Sketch-Based Image Retrieval, Triplet

We propose and evaluate several triplet CNN architectures for measuring the
similarity between sketches and photographs, within the context of the sketch
based image retrieval (SBIR) task. In contrast to recent fine-grained SBIR
work, we study the ability of our networks to generalise across diverse object
categories from limited training data, and explore in detail strategies for
weight sharing, pre-processing, data augmentation and dimensionality reduction.
We exceed the performance of pre-existing techniques on both the Flickr15k
category level SBIR benchmark by $18\%$, and the TU-Berlin SBIR benchmark by
$\sim10 \mathcal{T}_b$, when trained on the 250 category TU-Berlin
classification dataset augmented with 25k corresponding photographs harvested
from the Internet.
DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
Authors: Shu Zhang, Ran He, Tieniu Tan
Date: 2016-11-16 00:00:00
Tasks: Face Alignment, Face Verification, Facial Inpainting

MeshFace photos have been widely used in many Chinese business organizations
to protect ID face photos from being misused. The occlusions incurred by random
meshes severely degenerate the performance of face verification systems, which
raises the MeshFace verification problem between MeshFace and daily photos.
Previous methods cast this problem as a typical low-level vision problem, i.e.
blind inpainting. They recover perceptually pleasing clear ID photos from
MeshFaces by enforcing pixel level similarity between the recovered ID images
and the ground-truth clear ID images and then perform face verification on
them. Essentially, face verification is conducted on a compact feature space
rather than the image pixel space. Therefore, this paper argues that pixel
level similarity and feature level similarity jointly offer the key to improve
the verification performance. Based on this insight, we offer a novel feature
oriented blind face inpainting framework. Specifically, we implement this by
establishing a novel DeMeshNet, which consists of three parts. The first part
addresses blind inpainting of the MeshFaces by implicitly exploiting extra
supervision from the occlusion position to enforce pixel level similarity. The
second part explicitly enforces a feature level similarity in the compact
feature space, which can explore informative supervision from the feature space
to produce better inpainting results for verification. The last part copes with
face alignment within the net via a customized spatial transformer module when
extracting deep facial features. All the three parts are implemented within an
end-to-end network that facilitates efficient optimization. Extensive
experiments on two MeshFace datasets demonstrate the effectiveness of the
proposed DeMeshNet as well as the insight of this paper.
Temporal Convolutional Networks for Action Segmentation and Detection
Authors: Colin Lea, Michael D. Flynn, Rene Vidal, Austin Reiter, Gregory D. Hager
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Segmentation, Decoder, Skeleton Based Action Recognition

The ability to identify and temporally segment fine-grained human actions
throughout a video is crucial for robotics, surveillance, education, and
beyond. Typical approaches decouple this problem by first extracting local
spatiotemporal features from video frames and then feeding them into a temporal
classifier that captures high-level temporal patterns. We introduce a new class
of temporal models, which we call Temporal Convolutional Networks (TCNs), that
use a hierarchy of temporal convolutions to perform fine-grained action
segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling
to efficiently capture long-range temporal patterns whereas our Dilated TCN
uses dilated convolutions. We show that TCNs are capable of capturing action
compositions, segment durations, and long-range dependencies, and are over a
magnitude faster to train than competing LSTM-based Recurrent Neural Networks.
We apply these models to three challenging fine-grained datasets and show large
improvements over the state of the art.
How to do lexical quality estimation of a large OCRed historical Finnish newspaper collection with scarce resources
Authors: Kimmo Kettunen
Date: 2016-11-16 00:00:00
Tasks: Information Retrieval, Retrieval

The National Library of Finland has digitized the historical newspapers published in Finland between 1771 and 1910. This collection contains approximately 1.95 million pages in Finnish and Swedish. Finnish part of the collection consists of about 2.40 billion words. The National Library's Digital Collections are offered via the digi.kansalliskirjasto.fi web service, also known as Digi. Part of the newspaper material (from 1771 to 1874) is also available freely downloadable in The Language Bank of Finland provided by the FINCLARIN consortium. The collection can also be accessed through the Korp environment that has been developed by Spr{\aa}kbanken at the University of Gothenburg and extended by FINCLARIN team at the University of Helsinki to provide concordances of text resources. A Cranfield style information retrieval test collection has also been produced out of a small part of the Digi newspaper material at the University of Tampere. Quality of OCRed collections is an important topic in digital humanities, as it affects general usability and searchability of collections. There is no single available method to assess quality of large collections, but different methods can be used to approximate quality. This paper discusses different corpus analysis style methods to approximate overall lexical quality of the Finnish part of the Digi collection. Methods include usage of parallel samples and word error rates, usage of morphological analyzers, frequency analysis of words and comparisons to comparable edited lexical data. Our aim in the quality analysis is twofold: firstly to analyze the present state of the lexical data and secondly, to establish a set of assessment methods that build up a compact procedure for quality assessment after e.g. new OCRing or post correction of the material. In the discussion part of the paper we shall synthesize results of our different analyses.
Joint Network based Attention for Action Recognition
Authors: Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang
Date: 2016-11-16 00:00:00
Tasks: Action Recognition, Temporal Action Localization

By extracting spatial and temporal characteristics in one network, the
two-stream ConvNets can achieve the state-of-the-art performance in action
recognition. However, such a framework typically suffers from the separately
processing of spatial and temporal information between the two standalone
streams and is hard to capture long-term temporal dependence of an action. More
importantly, it is incapable of finding the salient portions of an action, say,
the frames that are the most discriminative to identify the action. To address
these problems, a \textbf{j}oint \textbf{n}etwork based \textbf{a}ttention
(JNA) is proposed in this study. We find that the fully-connected fusion,
branch selection and spatial attention mechanism are totally infeasible for
action recognition. Thus in our joint network, the spatial and temporal
branches share some information during the training stage. We also introduce an
attention mechanism on the temporal domain to capture the long-term dependence
meanwhile finding the salient portions. Extensive experiments are conducted on
two benchmark datasets, UCF101 and HMDB51. Experimental results show that our
method can improve the action recognition performance significantly and
achieves the state-of-the-art results on both datasets.
Deep Variational Inference Without Pixel-Wise Reconstruction
Authors: Siddharth Agrawal, Ambedkar Dukkipati
Date: 2016-11-16 00:00:00
Tasks: Variational Inference

Variational autoencoders (VAEs), that are built upon deep neural networks
have emerged as popular generative models in computer vision. Most of the work
towards improving variational autoencoders has focused mainly on making the
approximations to the posterior flexible and accurate, leading to tremendous
progress. However, there have been limited efforts to replace pixel-wise
reconstruction, which have known shortcomings. In this work, we use real-valued
non-volume preserving transformations (real NVP) to exactly compute the
conditional likelihood of the data given the latent distribution. We show that
a simple VAE with this form of reconstruction is competitive with complicated
VAE structures, on image modeling tasks. As part of our model, we develop
powerful conditional coupling layers that enable real NVP to learn with fewer
intermediate layers.
A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation
Authors: Avijit Dasgupta, Sonam Singh
Date: 2016-11-07 00:00:00
Tasks: Retinal Vessel Segmentation, Segmentation, Structured Prediction

Automatic segmentation of retinal blood vessels from fundus images plays an
important role in the computer aided diagnosis of retinal diseases. The task of
blood vessel segmentation is challenging due to the extreme variations in
morphology of the vessels against noisy background. In this paper, we formulate
the segmentation task as a multi-label inference task and utilize the implicit
advantages of the combination of convolutional neural networks and structured
prediction. Our proposed convolutional neural network based model achieves
strong performance and significantly outperforms the state-of-the-art for
automatic retinal blood vessel segmentation on DRIVE dataset with 95.33%
accuracy and 0.974 AUC score.
Driving CDCL Search
Authors: Carmine Dodaro, Philip Gasteiger, Nicola Leone, Benjamin Musitsch, Francesco Ricca, Konstantin Schekotihin
Date: 2016-11-16 00:00:00

The CDCL algorithm is the leading solution adopted by state-of-the-art
solvers for SAT, SMT, ASP, and others. Experiments show that the performance of
CDCL solvers can be significantly boosted by embedding domain-specific
heuristics, especially on large real-world problems. However, a proper
integration of such criteria in off-the-shelf CDCL implementations is not
obvious. In this paper, we distill the key ingredients that drive the search of
CDCL solvers, and propose a general framework for designing and implementing
new heuristics. We implemented our strategy in an ASP solver, and we
experimented on two industrial domains. On hard problem instances,
state-of-the-art implementations fail to find any solution in acceptable time,
whereas our implementation is very successful and finds all solutions.