pwc-archive/papers-with-abstracts huggingface dataset:
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
Authors: Maria Glenski, Ellyn Ayton, Dustin Arendt, Svitlana Volkova
Date: 2017-10-17 00:00:00
Tasks: Binary Classification, Clickbait Detection, object-detection, Object Detection

This paper presents the results and conclusions of our participation in the
Clickbait Challenge 2017 on automatic clickbait detection in social media. We
first describe linguistically-infused neural network models and identify
informative representations to predict the level of clickbaiting present in
Twitter posts. Our models allow to answer the question not only whether a post
is a clickbait or not, but to what extent it is a clickbait post e.g., not at
all, slightly, considerably, or heavily clickbaity using a score ranging from 0
to 1. We evaluate the predictive power of models trained on varied text and
image representations extracted from tweets. Our best performing model that
relies on the tweet text and linguistic markers of biased language extracted
from the tweet and the corresponding page yields mean squared error (MSE) of
0.04, mean absolute error (MAE) of 0.16 and R2 of 0.43 on the held-out test
data. For the binary classification setup (clickbait vs. non-clickbait), our
model achieved F1 score of 0.69. We have not found that image representations
combined with text yield significant performance improvement yet. Nevertheless,
this work is the first to present preliminary analysis of objects extracted
using Google Tensorflow object detection API from images in clickbait vs.
non-clickbait Twitter posts. Finally, we outline several steps to improve model
performance as a part of the future work.
Embedded Spectral Descriptors: Learning the point-wise correspondence metric via Siamese neural networks
Authors: Zhiyu Sun, Yusen He, Andrey Gritsenko, Amaury Lendasse, Stephen Baek
Date: 2017-10-17 00:00:00

A robust and informative local shape descriptor plays an important role in mesh registration. In this regard, spectral descriptors that are based on the spectrum of the Laplace-Beltrami operator have been a popular subject of research for the last decade due to their advantageous properties, such as isometry invariance. Despite such, however, spectral descriptors often fail to give a correct similarity measure for non-isometric cases where the metric distortion between the models is large. Hence, they are not reliable for correspondence matching problems when the models are not isometric. In this paper, it is proposed a method to improve the similarity metric of spectral descriptors for correspondence matching problems. We embed a spectral shape descriptor into a different metric space where the Euclidean distance between the elements directly indicates the geometric dissimilarity. We design and train a Siamese neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity. We demonstrate our approach can significantly enhance the performance of the conventional spectral descriptors by the simple augmentation achieved via the Siamese neural network in comparison to other state-of-the-art methods.
An Effective Training Method For Deep Convolutional Neural Network
Authors: Yang Jiang, Zeyang Dou, Qun Hao, Jie Cao, Kun Gao, Xi Chen
Date: 2017-07-31 00:00:00
Tasks: Object Recognition

In this paper, we propose the nonlinearity generation method to speed up and
stabilize the training of deep convolutional neural networks. The proposed
method modifies a family of activation functions as nonlinearity generators
(NGs). NGs make the activation functions linear symmetric for their inputs to
lower model capacity, and automatically introduce nonlinearity to enhance the
capacity of the model during training. The proposed method can be considered an
unusual form of regularization: the model parameters are obtained by training a
relatively low-capacity model, that is relatively easy to optimize at the
beginning, with only a few iterations, and these parameters are reused for the
initialization of a higher-capacity model. We derive the upper and lower bounds
of variance of the weight variation, and show that the initial symmetric
structure of NGs helps stabilize training. We evaluate the proposed method on
different frameworks of convolutional neural networks over two object
recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results
showed that the proposed method allows us to (1) speed up the convergence of
training, (2) allow for less careful weight initialization, (3) improve or at
least maintain the performance of the model at negligible extra computational
cost, and (4) easily train a very deep model.
Synthesizing Normalized Faces from Facial Identity Features
Authors: Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman
Date: 2017-01-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder

We present a method for synthesizing a frontal, neutral-expression image of a
person's face given an input face photograph. This is achieved by learning to
generate facial landmarks and textures from features extracted from a
facial-recognition network. Unlike previous approaches, our encoding feature
vector is largely invariant to lighting, pose, and facial expression.
Exploiting this invariance, we train our decoder network using only frontal,
neutral-expression photographs. Since these photographs are well aligned, we
can decompose them into a sparse set of landmark points and aligned texture
maps. The decoder then predicts landmarks and textures independently and
combines them using a differentiable image warping operation. The resulting
images can be used for a number of applications, such as analyzing facial
attributes, exposure and white balance adjustment, or creating a 3-D avatar.
A Convex Similarity Index for Sparse Recovery of Missing Image Samples
Authors: Amirhossein Javaheri, Hadi Zayyani, Farokh Marvasti
Date: 2017-01-25 00:00:00
Tasks: SSIM

This paper investigates the problem of recovering missing samples using
methods based on sparse representation adapted especially for image signals.
Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality
measure is used as the similarity criterion between the original and the
reconstructed images. The proposed criterion called Convex SIMilarity (CSIM)
index is a modified version of the Structural SIMilarity (SSIM) index, which
despite its predecessor, is convex and uni-modal. We derive mathematical
properties for the proposed index and show how to optimally choose the
parameters of the proposed criterion, investigating the Restricted Isometry
(RIP) and error-sensitivity properties. We also propose an iterative sparse
recovery method based on a constrained $l_1$-norm minimization problem,
incorporating CSIM as the fidelity criterion. The resulting convex optimization
problem is solved via an algorithm based on Alternating Direction Method of
Multipliers (ADMM). Taking advantage of the convexity of the CSIM index, we
also prove the convergence of the algorithm to the globally optimal solution of
the proposed optimization problem, starting from any arbitrary point.
Simulation results confirm the performance of the new similarity index as well
as the proposed algorithm for missing sample recovery of image patch signals.
Distributed algorithm for empty vehicles management in personal rapid transit (PRT) network
Authors: Wiktor B. Daszczuk, Jerzy Mieścicki, Waldemar Grabski
Date: 2017-10-17 00:00:00
Tasks: Management

In this paper, an original heuristic algorithm of empty vehicles management
in personal rapid transit network is presented. The algorithm is used for the
delivery of empty vehicles for waiting passengers, for balancing the
distribution of empty vehicles within the network, and for providing an empty
space for vehicles approaching a station. Each of these tasks involves a
decision on the trip that has to be done by a selected empty vehicle from its
actual location to some determined destination. The decisions are based on a
multi-parameter function involving a set of factors and thresholds. An
important feature of the algorithm is that it does not use any central database
of passenger input (demand) and locations of free vehicles. Instead, it is
based on the local exchange of data between stations: on their states and on
the vehicles they expect. Therefore, it seems well-tailored for a distributed
implementation. The algorithm is uniform, meaning that the same basic procedure
is used for multiple tasks using a task-specific set of parameters.
Paying Attention to Multi-Word Expressions in Neural Machine Translation
Authors: Matīss Rikters, Ondřej Bojar
Date: 2017-10-17 00:00:00
Proceeding: MTSummit 2017 9
Tasks: Machine Translation, NMT, Translation

Processing of multi-word expressions (MWEs) is a known problem for any natural language processing task. Even neural machine translation (NMT) struggles to overcome it. This paper presents results of experiments on investigating NMT attention allocation to the MWEs and improving automated translation of sentences that contain MWEs in English->Latvian and English->Czech NMT systems. Two improvement strategies were explored -(1) bilingual pairs of automatically extracted MWE candidates were added to the parallel corpus used to train the NMT system, and (2) full sentences containing the automatically extracted MWE candidates were added to the parallel corpus. Both approaches allowed to increase automated evaluation results. The best result - 0.99 BLEU point increase - has been reached with the first approach, while with the second approach minimal improvements achieved. We also provide open-source software and tools used for MWE extraction and alignment inspection.
Towards CT-quality Ultrasound Imaging using Deep Learning
Authors: Sanketh Vedula, Ortal Senouf, Alex M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky
Date: 2017-10-17 00:00:00
Tasks: Deep Learning, Medical Diagnosis

The cost-effectiveness and practical harmlessness of ultrasound imaging have
made it one of the most widespread tools for medical diagnosis. Unfortunately,
the beam-forming based image formation produces granular speckle noise,
blurring, shading and other artifacts. To overcome these effects, the ultimate
goal would be to reconstruct the tissue acoustic properties by solving a full
wave propagation inverse problem. In this work, we make a step towards this
goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result,
we are able to reconstruct CT-quality images from the reflected ultrasound
radio-frequency(RF) data obtained by simulation from real CT scans of a human
body. We also show that CNN is able to imitate existing computationally heavy
despeckling methods, thereby saving orders of magnitude in computations and
making them amenable to real-time applications.
Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance
Authors: Aditya Mogadala, Umanga Bista, Lexing Xie, Achim Rettinger
Date: 2017-10-17 00:00:00
Tasks: Caption Generation

Images in the wild encapsulate rich knowledge about varied abstract concepts
and cannot be sufficiently described with models built only using image-caption
pairs containing selected objects. We propose to handle such a task with the
guidance of a knowledge base that incorporate many abstract concepts. Our
method is a two-step process where we first build a multi-entity-label image
recognition model to predict abstract concepts as image labels and then
leverage them in the second step as an external semantic attention and
constrained inference in the caption generation model for describing images
that depict unseen/novel objects. Evaluations show that our models outperform
most of the prior work for out-of-domain captioning on MSCOCO and are useful
for integration of knowledge and vision in general.
Feature versus Raw Sequence: Deep Learning Comparative Study on Predicting Pre-miRNA
Authors: Jaya Thomas, Sonia Thomas, Lee Sael
Date: 2017-10-17 00:00:00
Tasks: Deep Learning

Should we input known genome sequence features or input sequence itself in
deep learning framework? As deep learning more popular in various applications,
researchers often come to question whether to generate features or use raw
sequences for deep learning. To answer this question, we study the prediction
accuracy of precursor miRNA prediction of feature-based deep belief network and
sequence-based convolution neural network. Tested on a variant of six-layer
convolution neural net and three-layer deep belief network, we find the raw
sequence input based convolution neural network model performs similar or
slightly better than feature based deep belief networks with best accuracy
values of 0.995 and 0.990, respectively. Both the models outperform existing
benchmarks models. The results shows us that if provided large enough data,
well devised raw sequence based deep learning models can replace feature based
deep learning models. However, construction of well behaved deep learning model
can be very challenging. In cased features can be easily extracted,
feature-based deep learning models may be a better alternative.
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Authors: Seokju Lee, Junsik Kim, Jae Shin Yoon, Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, In So Kweon
Date: 2017-10-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Lane Detection

In this paper, we propose a unified end-to-end trainable multi-task network
that jointly handles lane and road marking detection and recognition that is
guided by a vanishing point under adverse weather conditions. We tackle rainy
and low illumination conditions, which have not been extensively studied until
now due to clear challenges. For example, images taken under rainy days are
subject to low illumination, while wet roads cause light reflection and distort
the appearance of lane and road markings. At night, color distortion occurs
under limited illumination. As a result, no benchmark dataset exists and only a
few developed algorithms work under poor weather conditions. To address this
shortcoming, we build up a lane and road marking benchmark which consists of
about 20,000 images with 17 lane and road marking classes under four different
scenarios: no rain, rain, heavy rain, and night. We train and evaluate several
versions of the proposed multi-task network and validate the importance of each
task. The resulting approach, VPGNet, can detect and classify lanes and road
markings, and predict a vanishing point with a single forward pass.
Experimental results show that our approach achieves high accuracy and
robustness under various conditions in real-time (20 fps). The benchmark and
the VPGNet model will be publicly available.
A Comparative Analysis of Social Network Pages by Interests of Their Followers
Authors: Elena Mikhalkova, Nadezhda Ganzherli, Yuri Karyakin
Date: 2017-07-18 00:00:00
Tasks: General Classification

Being a matter of cognition, user interests should be apt to classification
independent of the language of users, social network and content of interest
itself. To prove it, we analyze a collection of English and Russian Twitter and
Vkontakte community pages by interests of their followers. First, we create a
model of Major Interests (MaIs) with the help of expert analysis and then
classify a set of pages using machine learning algorithms (SVM, Neural Network,
Naive Bayes, and some other). We take three interest domains that are typical
of both English and Russian-speaking communities: football, rock music,
vegetarianism. The results of classification show a greater correlation between
Russian-Vkontakte and Russian-Twitter pages while English-Twitterpages appear
to provide the highest score.
Precision Learning: Reconstruction Filter Kernel Discretization
Authors: Christopher Syben, Bernhard Stimpel, Katharina Breininger, Tobias Würfl, Rebecca Fahrig, Arnd Dörfler, Andreas Maier
Date: 2017-10-17 00:00:00
Tasks: Deep Learning

In this paper, we present substantial evidence that a deep neural network
will intrinsically learn the appropriate way to discretize the ideal continuous
reconstruction filter. Currently, the Ram-Lak filter or heuristic filters which
impose different noise assumptions are used for filtered back-projection. All
of these, however, inhibit a fully data-driven reconstruction deep learning
approach. In addition, the heuristic filters are not chosen in an optimal
sense. To tackle this issue, we propose a formulation to directly learn the
reconstruction filter. The filter is initialized with the ideal Ramp filter as
a strong pre-training and learned in frequency domain. We compare the learned
filter with the Ram-Lak and the Ramp filter on a numerical phantom as well as
on a real CT dataset. The results show that the network properly discretizes
the continuous Ramp filter and converges towards the Ram-Lak solution. In our
view these observations are interesting to gain a better understanding of deep
learning techniques and traditional analytic techniques such as Wiener
filtering and discretization theory. Furthermore, this will allow fully
trainable data-driven reconstruction deep learning approaches.
Best Viewpoint Tracking for Camera Mounted on Robotic Arm with Dynamic Obstacles
Authors: Christos Maniatis, Marcelo Saval-Calvo, Radim Tylecek, Robert B. Fisher
Date: 2017-08-01 00:00:00

The problem of finding a next best viewpoint for 3D modeling or scene mapping
has been explored in computer vision over the last decade. This paper tackles a
similar problem, but with different characteristics. It proposes a method for
dynamic next best viewpoint recovery of a target point while avoiding possible
occlusions. Since the environment can change, the method has to iteratively
find the next best view with a global understanding of the free and occupied
parts.
  We model the problem as a set of possible viewpoints which correspond to the
centers of the facets of a virtual tessellated hemisphere covering the scene.
Taking into account occlusions, distances between current and future
viewpoints, quality of the viewpoint and joint constraints (robot arm joint
distances or limits), we evaluate the next best viewpoint. The proposal has
been evaluated on 8 different scenarios with different occlusions and a short
3D video sequence to validate its dynamic performance.
Convergence Rate of Riemannian Hamiltonian Monte Carlo and Faster Polytope Volume Computation
Authors: Yin Tat Lee, Santosh S. Vempala
Date: 2017-10-17 00:00:00

We give the first rigorous proof of the convergence of Riemannian Hamiltonian
Monte Carlo, a general (and practical) method for sampling Gibbs distributions.
Our analysis shows that the rate of convergence is bounded in terms of natural
smoothness parameters of an associated Riemannian manifold. We then apply the
method with the manifold defined by the log barrier function to the problems of
(1) uniformly sampling a polytope and (2) computing its volume, the latter by
extending Gaussian cooling to the manifold setting. In both cases, the total
number of steps needed is O^{*}(mn^{\frac{2}{3}}), improving the state of the
art. A key ingredient of our analysis is a proof of an analog of the KLS
conjecture for Gibbs distributions over manifolds.
Single Shot Temporal Action Detection
Authors: Tianwei Lin, Xu Zhao, Zheng Shou
Date: 2017-10-17 00:00:00
Tasks: Action Detection, General Classification

Temporal action detection is a very important yet challenging problem, since
videos in real applications are usually long, untrimmed and contain multiple
action instances. This problem requires not only recognizing action categories
but also detecting start time and end time of each action instance. Many
state-of-the-art methods adopt the "detection by classification" framework:
first do proposal, and then classify proposals. The main drawback of this
framework is that the boundaries of action instance proposals have been fixed
during the classification step. To address this issue, we propose a novel
Single Shot Action Detector (SSAD) network based on 1D temporal convolutional
layers to skip the proposal generation step via directly detecting action
instances in untrimmed video. On pursuit of designing a particular SSAD network
that can work effectively for temporal action detection, we empirically search
for the best network architecture of SSAD due to lacking existing models that
can be directly adopted. Moreover, we investigate into input feature types and
fusion strategies to further improve detection accuracy. We conduct extensive
experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When
setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD
significantly outperforms other state-of-the-art systems by increasing mAP from
19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse
Authors: Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui
Date: 2017-09-06 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Language Modeling, Language Modelling, Word Embeddings

This study addresses the problem of identifying the meaning of unknown words
or entities in a discourse with respect to the word embedding approaches used
in neural language models. We proposed a method for on-the-fly construction and
exploitation of word embeddings in both the input and output layers of a neural
model by tracking contexts. This extends the dynamic entity representation used
in Kobayashi et al. (2016) and incorporates a copy mechanism proposed
independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we
construct a new task and dataset called Anonymized Language Modeling for
evaluating the ability to capture word meanings while reading. Experiments
conducted using our novel dataset show that the proposed variant of RNN
language model outperformed the baseline model. Furthermore, the experiments
also demonstrate that dynamic updates of an output layer help a model predict
reappearing entities, whereas those of an input layer are effective to predict
words following reappearing entities.
Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks
Authors: Marco Carraro, Matteo Munaro, Jeff Burke, Emanuele Menegatti
Date: 2017-10-17 00:00:00
Tasks: 2D Pose Estimation, 3D Pose Estimation, Pose Estimation

This paper proposes a novel system to estimate and track the 3D poses of
multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D
pose of each person is computed by a central node which receives the
single-view outcomes from each camera of the network. Each single-view outcome
is computed by using a CNN for 2D pose estimation and extending the resulting
skeletons to 3D by means of the sensor depth. The proposed system is
marker-less, multi-person, independent of background and does not make any
assumption on people appearance and initial pose. The system provides real-time
outcomes, thus being perfectly suited for applications requiring user
interaction. Experimental results show the effectiveness of this work with
respect to a baseline multi-view approach in different scenarios. To foster
research and applications based on this work, we released the source code in
OpenPTrack, an open source project for RGB-D people tracking.
Nonlinear Interference Mitigation via Deep Neural Networks
Authors: Christian Häger, Henry D. Pfister
Date: 2017-10-17 00:00:00

A neural-network-based approach is presented to efficiently implement digital
backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting
"learned" DBP significantly reduces the complexity compared to conventional DBP
implementations.