pwc-archive/papers-with-abstracts huggingface dataset:
Argotario: Computational Argumentation Meets Serious Games
Authors: Ivan Habernal, Raffael Hannemann, Christian Pollak, Christopher Klamm, Patrick Pauli, Iryna Gurevych
Date: 2017-07-19 00:00:00
Proceeding: EMNLP 2017 9

An important skill in critical thinking and argumentation is the ability to
spot and recognize fallacies. Fallacious arguments, omnipresent in
argumentative discourse, can be deceptive, manipulative, or simply leading to
`wrong moves' in a discussion. Despite their importance, argumentation scholars
and NLP researchers with focus on argumentation quality have not yet
investigated fallacies empirically. The nonexistence of resources dealing with
fallacious argumentation calls for scalable approaches to data acquisition and
annotation, for which the serious games methodology offers an appealing, yet
unexplored, alternative. We present Argotario, a serious game that deals with
fallacies in everyday argumentation. Argotario is a multilingual, open-source,
platform-independent application with strong educational aspects, accessible at
www.argotario.net.
Probably approximate Bayesian computation: nonasymptotic convergence of ABC under misspecification
Authors: James Ridgway
Date: 2017-07-19 00:00:00

Approximate Bayesian computation (ABC) is a widely used inference method in
Bayesian statistics to bypass the point-wise computation of the likelihood. In
this paper we develop theoretical bounds for the distance between the
statistics used in ABC. We show that some versions of ABC are inherently robust
to misspecification. The bounds are given in the form of oracle inequalities
for a finite sample size. The dependence on the dimension of the parameter
space and the number of statistics is made explicit. The results are shown to
be amenable to oracle inequalities in parameter space. We apply our theoretical
results to given prior distributions and data generating processes, including a
non-parametric regression model. In a second part of the paper, we propose a
sequential Monte Carlo (SMC) to sample from the pseudo-posterior, improving
upon the state of the art samplers.
Orthogonal and Idempotent Transformations for Learning Deep Neural Networks
Authors: Jingdong Wang, Yajie Xing, Kexin Zhang, Cha Zhang
Date: 2017-07-19 00:00:00

Identity transformations, used as skip-connections in residual networks,
directly connect convolutional layers close to the input and those close to the
output in deep neural networks, improving information flow and thus easing the
training. In this paper, we introduce two alternative linear transforms,
orthogonal transformation and idempotent transformation. According to the
definition and property of orthogonal and idempotent matrices, the product of
multiple orthogonal (same idempotent) matrices, used to form linear
transformations, is equal to a single orthogonal (idempotent) matrix, resulting
in that information flow is improved and the training is eased. One interesting
point is that the success essentially stems from feature reuse and gradient
reuse in forward and backward propagation for maintaining the information
during flow and eliminating the gradient vanishing problem because of the
express way through skip-connections. We empirically demonstrate the
effectiveness of the proposed two transformations: similar performance in
single-branch networks and even superior in multi-branch networks in comparison
to identity transformations.
Fast Screening Algorithm for Rotation and Scale Invariant Template Matching
Authors: Bolin Liu, Xiao Shu, Xiaolin Wu
Date: 2017-07-18 00:00:00
Tasks: Computational Efficiency, Template Matching

This paper presents a generic pre-processor for expediting conventional
template matching techniques. Instead of locating the best matched patch in the
reference image to a query template via exhaustive search, the proposed
algorithm rules out regions with no possible matches with minimum computational
efforts. While working on simple patch features, such as mean, variance and
gradient, the fast pre-screening is highly discriminative. Its computational
efficiency is gained by using a novel octagonal-star-shaped template and the
inclusion-exclusion principle to extract and compare patch features. Moreover,
it can handle arbitrary rotation and scaling of reference images effectively.
Extensive experiments demonstrate that the proposed algorithm greatly reduces
the search space while never missing the best match.
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
Authors: Emilie Gerardin, Gaël Chételat, Marie Chupin, Rémi Cuingnet, Béatrice Desgranges, Ho-Sung Kim, Marc Niethammer, Bruno Dubois, Stéphane Lehéricy, Line Garnero, Francis Eustache, Olivier Colliot
Date: 2017-07-19 00:00:00
Tasks: Classification, General Classification, Sensitivity, Specificity

We describe a new method to automatically discriminate between patients with
Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly
controls, based on multidimensional classification of hippocampal shape
features. This approach uses spherical harmonics (SPHARM) coefficients to model
the shape of the hippocampi, which are segmented from magnetic resonance images
(MRI) using a fully automatic method that we previously developed. SPHARM
coefficients are used as features in a classification procedure based on
support vector machines (SVM). The most relevant features for classification
are selected using a bagging strategy. We evaluate the accuracy of our method
in a group of 23 patients with AD (10 males, 13 females, age $\pm$
standard-deviation (SD) = 73 $\pm$ 6 years, mini-mental score (MMS) = 24.4
$\pm$ 2.8), 23 patients with amnestic MCI (10 males, 13 females, age $\pm$ SD =
74 $\pm$ 8 years, MMS = 27.3 $\pm$ 1.4) and 25 elderly healthy controls (13
males, 12 females, age $\pm$ SD = 64 $\pm$ 8 years), using leave-one-out
cross-validation. For AD vs controls, we obtain a correct classification rate
of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we
obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of
84%. This accuracy is superior to that of hippocampal volumetry and is
comparable to recently published SVM-based whole-brain classification methods,
which relied on a different strategy. This new method may become a useful tool
to assist in the diagnosis of Alzheimer's disease.
Deep Convolutional Neural Networks with Merge-and-Run Mappings
Authors: Liming Zhao, Jingdong Wang, Xi Li, Zhuowen Tu, Wen-Jun Zeng
Date: 2016-11-23 00:00:00

A deep residual network, built by stacking a sequence of residual blocks, is
easy to train, because identity mappings skip residual branches and thus
improve information flow. To further reduce the training difficulty, we present
a simple network architecture, deep merge-and-run neural networks. The novelty
lies in a modularized building block, merge-and-run block, which assembles
residual branches in parallel through a merge-and-run mapping: Average the
inputs of these residual branches (Merge), and add the average to the output of
each residual branch as the input of the subsequent residual branch (Run),
respectively. We show that the merge-and-run mapping is a linear idempotent
function in which the transformation matrix is idempotent, and thus improves
information flow, making training easy. In comparison to residual networks, our
networks enjoy compelling advantages: they contain much shorter paths, and the
width, i.e., the number of channels, is increased. We evaluate the performance
on the standard recognition tasks. Our approach demonstrates consistent
improvements over ResNets with the comparable setup, and achieves competitive
results (e.g., $3.57\%$ testing error on CIFAR-$10$, $19.00\%$ on CIFAR-$100$,
$1.51\%$ on SVHN).
When Unsupervised Domain Adaptation Meets Tensor Representations
Authors: Hao Lu, Lei Zhang, Zhiguo Cao, Wei Wei, Ke Xian, Chunhua Shen, Anton Van Den Hengel
Date: 2017-07-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Domain adaption (DA) allows machine learning methods trained on data sampled
from one distribution to be applied to data sampled from another. It is thus of
great practical importance to the application of such methods. Despite the fact
that tensor representations are widely used in Computer Vision to capture
multi-linear relationships that affect the data, most existing DA methods are
applicable to vectors only. This renders them incapable of reflecting and
preserving important structure in many problems. We thus propose here a
learning-based method to adapt the source and target tensor representations
directly, without vectorization. In particular, a set of alignment matrices is
introduced to align the tensor representations from both domains into the
invariant tensor subspace. These alignment matrices and the tensor subspace are
modeled as a joint optimization problem and can be learned adaptively from the
data using the proposed alternative minimization scheme. Extensive experiments
show that our approach is capable of preserving the discriminative power of the
source domain, of resisting the effects of label noise, and works effectively
for small sample sizes, and even one-shot DA. We show that our method
outperforms the state-of-the-art on the task of cross-domain visual recognition
in both efficacy and efficiency, and particularly that it outperforms all
comparators when applied to DA of the convolutional activations of deep
convolutional networks.
Image Projective Invariants
Authors: Erbo Li, Hanlin Mo, Dong Xu, Hua Li
Date: 2017-07-19 00:00:00
Tasks: Image Retrieval, Retrieval

In this paper, we propose relative projective differential invariants (RPDIs)
which are invariant to general projective transformations. By using RPDIs and
the structural frame of integral invariant, projective weighted moment
invariants (PIs) can be constructed very easily. It is first proved that a kind
of projective invariants exists in terms of weighted integration of images,
with relative differential invariants as the weight functions. Then, some
simple instances of PIs are given. In order to ensure the stability and
discriminability of PIs, we discuss how to calculate partial derivatives of
discrete images more accurately. Since the number of pixels in discrete images
before and after the geometric transformation may be different, we design the
method to normalize the number of pixels. These ways enhance the performance of
PIs. Finally, we carry out some experiments based on synthetic and real image
datasets. We choose commonly used moment invariants for comparison. The results
indicate that PIs have better performance than other moment invariants in image
retrieval and classification. With PIs, one can compare the similarity between
images under the projective transformation without knowing the parameters of
the transformation, which provides a good tool to shape analysis in image
processing, computer vision and pattern recognition.
Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints
Authors: Wenlong Mou, Li-Wei Wang, Xiyu Zhai, Kai Zheng
Date: 2017-07-19 00:00:00
Tasks: Generalization Bounds, Learning Theory, Vocal Bursts Valence Prediction

Algorithm-dependent generalization error bounds are central to statistical
learning theory. A learning algorithm may use a large hypothesis space, but the
limited number of iterations controls its model capacity and generalization
error. The impacts of stochastic gradient methods on generalization error for
non-convex learning problems not only have important theoretical consequences,
but are also critical to generalization errors of deep learning.
  In this paper, we study the generalization errors of Stochastic Gradient
Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed
with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian
results respectively. The stability-based theory obtains a bound of
$O\left(\frac{1}{n}L\sqrt{\beta T_k}\right)$, where $L$ is uniform Lipschitz
parameter, $\beta$ is inverse temperature, and $T_k$ is aggregated step sizes.
For PAC-Bayesian theory, though the bound has a slower $O(1/\sqrt{n})$ rate,
the contribution of each step is shown with an exponentially decaying factor by
imposing $\ell^2$ regularization, and the uniform Lipschitz constant is also
replaced by actual norms of gradients along trajectory. Our bounds have no
implicit dependence on dimensions, norms or other capacity measures of
parameter, which elegantly characterizes the phenomenon of "Fast Training
Guarantees Generalization" in non-convex settings. This is the first
algorithm-dependent result with reasonable dependence on aggregated step sizes
for non-convex learning, and has important implications to statistical learning
aspects of stochastic gradient methods in complicated models such as deep
learning.
Face Alignment Robust to Pose, Expressions and Occlusions
Authors: Vishnu Naresh Boddeti, Myung-Cheol Roh, Jongju Shin, Takaharu Oguri, Takeo Kanade
Date: 2017-07-19 00:00:00
Tasks: Face Alignment

We propose an Ensemble of Robust Constrained Local Models for alignment of
faces in the presence of significant occlusions and of any unknown pose and
expression. To account for partial occlusions we introduce, Robust Constrained
Local Models, that comprises of a deformable shape and local landmark
appearance model and reasons over binary occlusion labels. Our occlusion
reasoning proceeds by a hypothesize-and-test search over occlusion labels.
Hypotheses are generated by Constrained Local Model based shape fitting over
randomly sampled subsets of landmark detector responses and are evaluated by
the quality of face alignment. To span the entire range of facial pose and
expression variations we adopt an ensemble of independent Robust Constrained
Local Models to search over a discretized representation of pose and
expression. We perform extensive evaluation on a large number of face images,
both occluded and unoccluded. We find that our face alignment system trained
entirely on facial images captured "in-the-lab" exhibits a high degree of
generalization to facial images captured "in-the-wild". Our results are
accurate and stable over a wide spectrum of occlusions, pose and expression
variations resulting in excellent performance on many real-world face datasets.
Hybrid PS-V Technique: A Novel Sensor Fusion Approach for Fast Mobile Eye-Tracking with Sensor-Shift Aware Correction
Authors: Ioannis Rigas, Hayes Raffle, Oleg V. Komogortsev
Date: 2017-07-17 00:00:00
Tasks: Sensor Fusion

This paper introduces and evaluates a hybrid technique that fuses efficiently
the eye-tracking principles of photosensor oculography (PSOG) and video
oculography (VOG). The main concept of this novel approach is to use a few fast
and power-economic photosensors as the core mechanism for performing high speed
eye-tracking, whereas in parallel, use a video sensor operating at low
sampling-rate (snapshot mode) to perform dead-reckoning error correction when
sensor movements occur. In order to evaluate the proposed method, we simulate
the functional components of the technique and present our results in
experimental scenarios involving various combinations of horizontal and
vertical eye and sensor movements. Our evaluation shows that the developed
technique can be used to provide robustness to sensor shifts that otherwise
could induce error larger than 5 deg. Our analysis suggests that the technique
can potentially enable high speed eye-tracking at low power profiles, making it
suitable to be used in emerging head-mounted devices, e.g. AR/VR headsets.
Photosensor Oculography: Survey and Parametric Analysis of Designs using Model-Based Simulation
Authors: Ioannis Rigas, Hayes Raffle, Oleg V. Komogortsev
Date: 2017-07-17 00:00:00

This paper presents a renewed overview of photosensor oculography (PSOG), an
eye-tracking technique based on the principle of using simple photosensors to
measure the amount of reflected (usually infrared) light when the eye rotates.
Photosensor oculography can provide measurements with high precision, low
latency and reduced power consumption, and thus it appears as an attractive
option for performing eye-tracking in the emerging head-mounted interaction
devices, e.g. augmented and virtual reality (AR/VR) headsets. In our current
work we employ an adjustable simulation framework as a common basis for
performing an exploratory study of the eye-tracking behavior of different
photosensor oculography designs. With the performed experiments we explore the
effects from the variation of some basic parameters of the designs on the
resulting accuracy and cross-talk, which are crucial characteristics for the
seamless operation of human-computer interaction applications based on
eye-tracking. Our experimental results reveal the design trade-offs that need
to be adopted to tackle the competing conditions that lead to optimum
performance of different eye-tracking characteristics. We also present the
transformations that arise in the eye-tracking output when sensor shifts occur,
and assess the resulting degradation in accuracy for different combinations of
eye movements and sensor shifts.
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Authors: Yanmin Qian, Xuankai Chang, Dong Yu
Date: 2017-07-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Separation

Although great progresses have been made in automatic speech recognition
(ASR), significant performance degradation is still observed when recognizing
multi-talker mixed speech. In this paper, we propose and evaluate several
architectures to address this problem under the assumption that only a single
channel of mixed signal is available. Our technique extends permutation
invariant training (PIT) by introducing the front-end feature separation module
with the minimum mean square error (MSE) criterion and the back-end recognition
module with the minimum cross entropy (CE) criterion. More specifically, during
training we compute the average MSE or CE over the whole utterance for each
possible utterance-level output-target assignment, pick the one with the
minimum MSE or CE, and optimize for that assignment. This strategy elegantly
solves the label permutation problem observed in the deep learning based
multi-talker mixed speech separation and recognition systems. The proposed
architectures are evaluated and compared on an artificially mixed AMI dataset
with both two- and three-talker mixed speech. The experimental results indicate
that our proposed architectures can cut the word error rate (WER) by 45.0% and
25.0% relatively against the state-of-the-art single-talker speech recognition
system across all speakers when their energies are comparable, for two- and
three-talker mixed speech, respectively. To our knowledge, this is the first
work on the multi-talker mixed speech recognition on the challenging
speaker-independent spontaneous large vocabulary continuous speech task.
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes
Authors: Tomoharu Iwata, Zoubin Ghahramani
Date: 2017-07-19 00:00:00
Tasks: Gaussian Processes, Variational Inference

We propose a simple method that combines neural networks and Gaussian
processes. The proposed method can estimate the uncertainty of outputs and
flexibly adjust target functions where training data exist, which are
advantages of Gaussian processes. The proposed method can also achieve high
generalization performance for unseen input configurations, which is an
advantage of neural networks. With the proposed method, neural networks are
used for the mean functions of Gaussian processes. We present a scalable
stochastic inference procedure, where sparse Gaussian processes are inferred by
stochastic variational inference, and the parameters of neural networks and
kernels are estimated by stochastic gradient descent methods, simultaneously.
We use two real-world spatio-temporal data sets to demonstrate experimentally
that the proposed method achieves better uncertainty estimation and
generalization performance than neural networks and Gaussian processes.
The Role of Conversation Context for Sarcasm Detection in Online Interactions
Authors: Debanjan Ghosh, Alexander Richard Fabbri, Smaranda Muresan
Date: 2017-07-19 00:00:00
Proceeding: WS 2017 8
Tasks: Sarcasm Detection, Sentence

Computational models for sarcasm detection have often relied on the content
of utterances in isolation. However, speaker's sarcastic intent is not always
obvious without additional context. Focusing on social media discussions, we
investigate two issues: (1) does modeling of conversation context help in
sarcasm detection and (2) can we understand what part of conversation context
triggered the sarcastic reply. To address the first issue, we investigate
several types of Long Short-Term Memory (LSTM) networks that can model both the
conversation context and the sarcastic response. We show that the conditional
LSTM network (Rocktaschel et al., 2015) and LSTM networks with sentence level
attention on context and response outperform the LSTM model that reads only the
response. To address the second issue, we present a qualitative analysis of
attention weights produced by the LSTM models with attention and discuss the
results compared with human performance on the task.
Recognizing and Curating Photo Albums via Event-Specific Image Importance
Authors: Yufei Wang, Zhe Lin, Xiaohui Shen, Radomir Mech, Gavin Miller, Garrison W. Cottrell
Date: 2017-07-19 00:00:00
Tasks: Prediction, Vocal Bursts Type Prediction

Automatic organization of personal photos is a problem with many real world
ap- plications, and can be divided into two main tasks: recognizing the event
type of the photo collection, and selecting interesting images from the
collection. In this paper, we attempt to simultaneously solve both tasks:
album-wise event recognition and image- wise importance prediction. We
collected an album dataset with both event type labels and image importance
labels, refined from an existing CUFED dataset. We propose a hybrid system
consisting of three parts: A siamese network-based event-specific image
importance prediction, a Convolutional Neural Network (CNN) that recognizes the
event type, and a Long Short-Term Memory (LSTM)-based sequence level event
recognizer. We propose an iterative updating procedure for event type and image
importance score prediction. We experimentally verified that image importance
score prediction and event type recognition can each help the performance of
the other.
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior
Authors: Felipe Tobar, Gonzalo Rios, Tomás Valdivia, Pablo Guerrero
Date: 2017-07-19 00:00:00
Tasks: Bayesian Inference, Time Series, Time Series Analysis

In sensing applications, sensors cannot always measure the latent quantity of
interest at the required resolution, sometimes they can only acquire a blurred
version of it due the sensor's transfer function. To recover latent signals
when only noisy mixed measurements of the signal are available, we propose the
Gaussian process mixture of measurements (GPMM), which models the latent signal
as a Gaussian process (GP) and allows us to perform Bayesian inference on such
signal conditional to a set of noisy mixture of measurements. We describe how
to train GPMM, that is, to find the hyperparameters of the GP and the mixing
weights, and how to perform inference on the latent signal under GPMM;
additionally, we identify the solution to the underdetermined linear system
resulting from a sensing application as a particular case of GPMM. The proposed
model is validated in the recovery of three signals: a smooth synthetic signal,
a real-world heart-rate time series and a step function, where GPMM
outperformed the standard GP in terms of estimation error, uncertainty
representation and recovery of the spectral content of the latent signal.