pwc-archive/papers-with-abstracts huggingface dataset:
Relevance in Structured Argumentation
Authors: AnneMarie Borg, Christian Straßer
Date: 2018-09-13 00:00:00

We study properties related to relevance in non-monotonic consequence relations obtained by systems of structured argumentation. Relevance desiderata concern the robustness of a consequence relation under the addition of irrelevant information. For an account of what (ir)relevance amounts to we use syntactic and semantic considerations. Syntactic criteria have been proposed in the domain of relevance logic and were recently used in argumentation theory under the names of non-interference and crash-resistance. The basic idea is that the conclusions of a given argumentative theory should be robust under adding information that shares no propositional variables with the original database. Some semantic relevance criteria are known from non-monotonic logic. For instance, cautious monotony states that if we obtain certain conclusions from an argumentation theory, we may expect to still obtain the same conclusions if we add some of them to the given database. In this paper we investigate properties of structured argumentation systems that warrant relevance desiderata.
Stochastic Variational Optimization
Authors: Thomas Bird, Julius Kunze, David Barber
Date: 2018-09-13 00:00:00

Variational Optimization forms a differentiable upper bound on an objective.
We show that approaches such as Natural Evolution Strategies and Gaussian
Perturbation, are special cases of Variational Optimization in which the
expectations are approximated by Gaussian sampling. These approaches are of
particular interest because they are parallelizable. We calculate the
approximate bias and variance of the corresponding gradient estimators and
demonstrate that using antithetic sampling or a baseline is crucial to mitigate
their problems. We contrast these methods with an alternative parallelizable
method, namely Directional Derivatives. We conclude that, for differentiable
objectives, using Directional Derivatives is preferable to using Variational
Optimization to perform parallel Stochastic Gradient Descent.
On Offline Evaluation of Vision-based Driving Models
Authors: Felipe Codevilla, Antonio M. López, Vladlen Koltun, Alexey Dosovitskiy
Date: 2018-09-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Autonomous Driving

Autonomous driving models should ideally be evaluated by deploying them on a
fleet of physical vehicles in the real world. Unfortunately, this approach is
not practical for the vast majority of researchers. An attractive alternative
is to evaluate models offline, on a pre-collected validation dataset with
ground truth annotation. In this paper, we investigate the relation between
various online and offline metrics for evaluation of autonomous driving models.
We find that offline prediction error is not necessarily correlated with
driving quality, and two models with identical prediction error can differ
dramatically in their driving performance. We show that the correlation of
offline evaluation with driving quality can be significantly improved by
selecting an appropriate validation dataset and suitable offline metrics. The
supplementary video can be viewed at
https://www.youtube.com/watch?v=P8K8Z-iF0cY
Tübingen-Oslo system: Linear regression works the best at Predicting Current and Future Psychological Health from Childhood Essays in the CLPsych 2018 Shared Task
Authors: Çağrı Çöltekin, Taraka Rama
Date: 2018-09-13 00:00:00
Tasks: regression

This paper describes our efforts in predicting current and future
psychological health from childhood essays within the scope of the CLPsych-2018
Shared Task. We experimented with a number of different models, including
recurrent and convolutional networks, Poisson regression, support vector
regression, and L1 and L2 regularized linear regression. We obtained the best
results on the training/development data with L2 regularized linear regression
(ridge regression) which also got the best scores on main metrics in the
official testing for task A (predicting psychological health from essays
written at the age of 11 years) and task B (predicting later psychological
health from essays written at the age of 11).
Image Captioning based on Deep Reinforcement Learning
Authors: Haichao Shi, Peng Li, Bo wang, Zhenyu Wang
Date: 2018-09-13 00:00:00
Tasks: Deep Reinforcement Learning, Image Captioning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently it has shown that the policy-gradient methods for reinforcement
learning have been utilized to train deep end-to-end systems on natural
language processing tasks. What's more, with the complexity of understanding
image content and diverse ways of describing image content in natural language,
image captioning has been a challenging problem to deal with. To the best of
our knowledge, most state-of-the-art methods follow a pattern of sequential
model, such as recurrent neural networks (RNN). However, in this paper, we
propose a novel architecture for image captioning with deep reinforcement
learning to optimize image captioning tasks. We utilize two networks called
"policy network" and "value network" to collaboratively generate the captions
of images. The experiments are conducted on Microsoft COCO dataset, and the
experimental results have verified the effectiveness of the proposed method.
Bayesian Structure Learning by Recursive Bootstrap
Authors: Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Guy Koren, Gal Novik
Date: 2018-09-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Computational Efficiency, Model Selection

We address the problem of Bayesian structure learning for domains with
hundreds of variables by employing non-parametric bootstrap, recursively. We
propose a method that covers both model averaging and model selection in the
same framework. The proposed method deals with the main weakness of
constraint-based learning---sensitivity to errors in the independence
tests---by a novel way of combining bootstrap with constraint-based learning.
Essentially, we provide an algorithm for learning a tree, in which each node
represents a scored CPDAG for a subset of variables and the level of the node
corresponds to the maximal order of conditional independencies that are encoded
in the graph. As higher order independencies are tested in deeper recursive
calls, they benefit from more bootstrap samples, and therefore more resistant
to the curse-of-dimensionality. Moreover, the re-use of stable low order
independencies allows greater computational efficiency. We also provide an
algorithm for sampling CPDAGs efficiently from their posterior given the
learned tree. We empirically demonstrate that the proposed algorithm scales
well to hundreds of variables, and learns better MAP models and more reliable
causal relationships between variables, than other state-of-the-art-methods.
Canonical and Compact Point Cloud Representation for Shape Classification
Authors: Kent Fujiwara, Ikuro Sato, Mitsuru Ambai, Yuichi Yoshida, Yoshiaki Sakakura
Date: 2018-09-13 00:00:00
Tasks: Classification, General Classification

We present a novel compact point cloud representation that is inherently
invariant to scale, coordinate change and point permutation. The key idea is to
parametrize a distance field around an individual shape into a unique,
canonical, and compact vector in an unsupervised manner. We firstly project a
distance field to a $4$D canonical space using singular value decomposition. We
then train a neural network for each instance to non-linearly embed its
distance field into network parameters. We employ a bias-free Extreme Learning
Machine (ELM) with ReLU activation units, which has scale-factor commutative
property between layers. We demonstrate the descriptiveness of the
instance-wise, shape-embedded network parameters by using them to classify
shapes in $3$D datasets. Our learning-based representation requires minimal
augmentation and simple neural networks, where previous approaches demand
numerous representations to handle coordinate change and point permutation.
Focus Group on Artificial Intelligence for Health
Authors: Marcel Salathé, Thomas Wiegand, Markus Wenzel
Date: 2018-09-13 00:00:00
Tasks: Decision Making

Artificial Intelligence (AI) - the phenomenon of machines being able to solve
problems that require human intelligence - has in the past decade seen an
enormous rise of interest due to significant advances in effectiveness and use.
The health sector, one of the most important sectors for societies and
economies worldwide, is particularly interesting for AI applications, given the
ongoing digitalisation of all types of health information. The potential for AI
assistance in the health domain is immense, because AI can support medical
decision making at reduced costs, everywhere. However, due to the complexity of
AI algorithms, it is difficult to distinguish good from bad AI-based solutions
and to understand their strengths and weaknesses, which is crucial for
clarifying responsibilities and for building trust. For this reason, the
International Telecommunication Union (ITU) has established a new Focus Group
on "Artificial Intelligence for Health" (FG-AI4H) in partnership with the World
Health Organization (WHO). Health and care services are usually the
responsibility of a government - even when provided through private insurance
systems - and thus under the responsibility of WHO/ITU member states. FG-AI4H
will identify opportunities for international standardization, which will
foster the application of AI to health issues on a global scale. In particular,
it will establish a standardized assessment framework with open benchmarks for
the evaluation of AI-based methods for health, such as AI-based diagnosis,
triage or treatment decisions.
Adversarial Examples: Opportunities and Challenges
Authors: Jiliang Zhang, Chen Li
Date: 2018-09-13 00:00:00
Tasks: Autonomous Vehicles, Deep Learning, Medical Diagnosis

Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from real examples, AEs can mislead the model to predict incorrect outputs while hardly be distinguished by human eyes, therefore threaten security-critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of artificial intelligence (AI) security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristics and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that, we review the existing defenses and discuss their limitations. Finally, future research opportunities and challenges on AEs are prospected.
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
Authors: Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee
Date: 2018-09-13 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recently, it has been shown that in super-resolution, there exists a tradeoff
relationship between the quantitative and perceptual quality of super-resolved
images, which correspond to the similarity to the ground-truth images and the
naturalness, respectively. In this paper, we propose a novel super-resolution
method that can improve the perceptual quality of the upscaled images while
preserving the conventional quantitative performance. The proposed method
employs a deep network for multi-pass upscaling in company with a discriminator
network and two quantitative score predictor networks. Experimental results
demonstrate that the proposed method achieves a good balance of the
quantitative and perceptual quality, showing more satisfactory results than
existing methods.
Generative adversarial network-based image super-resolution using perceptual content losses
Authors: Manri Cheon, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee
Date: 2018-09-13 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

In this paper, we propose a deep generative adversarial network for
super-resolution considering the trade-off between perception and distortion.
Based on good performance of a recently developed model for super-resolution,
i.e., deep residual network using enhanced upscale modules (EUSR), the proposed
model is trained to improve perceptual performance with only slight increase of
distortion. For this purpose, together with the conventional content loss,
i.e., reconstruction loss such as L1 or L2, we consider additional losses in
the training phase, which are the discrete cosine transform coefficients loss
and differential content loss. These consider perceptual part in the content
loss, i.e., consideration of proper high frequency components is helpful for
the trade-off problem in super-resolution. The experimental results show that
our proposed model has good performance for both perception and distortion, and
is effective in perceptual super-resolution applications.
Towards Coinductive Theory Exploration in Horn Clause Logic: Position Paper
Authors: Ekaterina Komendantskaya Dr, Yue Li
Date: 2018-09-13 00:00:00
Tasks: Position

Coinduction occurs in two guises in Horn clause logic: in proofs of
self-referencing properties and relations, and in proofs involving construction
of (possibly irregular) infinite data. Both instances of coinductive reasoning
appeared in the literature before, but a systematic analysis of these two kinds
of proofs and of their relation was lacking. We propose a general
proof-theoretic framework for handling both kinds of coinduction arising in
Horn clause logic. To this aim, we propose a coinductive extension of Miller et
al's framework of uniform proofs and prove its soundness relative to
coinductive models of Horn clause logic.
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Authors: Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek, Tom Drummond, Chunhua Shen, Ian Reid
Date: 2018-09-13 00:00:00
Tasks: Depth Estimation, GPU, Knowledge Distillation, Monocular Depth Estimation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation, Surface Normals Estimation

Deployment of deep learning models in robotics as sensory information
extractors can be a daunting task to handle, even using generic GPU cards.
Here, we address three of its most prominent hurdles, namely, i) the adaptation
of a single model to perform multiple tasks at once (in this work, we consider
depth estimation and semantic segmentation crucial for acquiring geometric and
semantic understanding of the scene), while ii) doing it in real-time, and iii)
using asymmetric datasets with uneven numbers of annotations per each modality.
To overcome the first two issues, we adapt a recently proposed real-time
semantic segmentation network, making changes to further reduce the number of
floating point operations. To approach the third issue, we embrace a simple
solution based on hard knowledge distillation under the assumption of having
access to a powerful `teacher' network. We showcase how our system can be
easily extended to handle more tasks, and more datasets, all at once,
performing depth estimation and segmentation both indoors and outdoors with a
single model. Quantitatively, we achieve results equivalent to (or better than)
current state-of-the-art approaches with one forward pass costing just 13ms and
6.5 GFLOPs on 640x480 inputs. This efficiency allows us to directly incorporate
the raw predictions of our network into the SemanticFusion framework for dense
3D semantic reconstruction of the scene.
Video to Fully Automatic 3D Hair Model
Authors: Shu Liang, Xiufeng Huang, Xianyu Meng, Kunyao Chen, Linda G. Shapiro, Ira Kemelmacher-Shlizerman
Date: 2018-09-13 00:00:00
Tasks: model

Imagine taking a selfie video with your mobile phone and getting as output a
3D model of your head (face and 3D hair strands) that can be later used in VR,
AR, and any other domain. State of the art hair reconstruction methods allow
either a single photo (thus compromising 3D quality) or multiple views, but
they require manual user interaction (manual hair segmentation and capture of
fixed camera views that span full 360 degree). In this paper, we describe a
system that can completely automatically create a reconstruction from any video
(even a selfie video), and we don't require specific views, since taking your
-90 degree, 90 degree, and full back views is not feasible in a selfie capture.
  In the core of our system, in addition to the automatization components, hair
strands are estimated and deformed in 3D (rather than 2D as in state of the
art) thus enabling superior results. We provide qualitative, quantitative, and
Mechanical Turk human studies that support the proposed system, and show
results on a diverse variety of videos (8 different celebrity videos, 9 selfie
mobile videos, spanning age, gender, hair length, type, and styling).
3D Face Hallucination from a Single Depth Frame
Authors: Shu Liang, Ira Kemelmacher-Shlizerman, Linda G. Shapiro
Date: 2018-09-13 00:00:00
Tasks: Face Hallucination, Hallucination

We present an algorithm that takes a single frame of a person's face from a
depth camera, e.g., Kinect, and produces a high-resolution 3D mesh of the input
face. We leverage a dataset of 3D face meshes of 1204 distinct individuals
ranging from age 3 to 40, captured in a neutral expression. We divide the input
depth frame into semantically significant regions (eyes, nose, mouth, cheeks)
and search the database for the best matching shape per region. We further
combine the input depth frame with the matched database shapes into a single
mesh that results in a high-resolution shape of the input person. Our system is
fully automatic and uses only depth data for matching, making it invariant to
imaging conditions. We evaluate our results using ground truth shapes, as well
as compare to state-of-the-art shape estimation methods. We demonstrate the
robustness of our local matching approach with high-quality reconstruction of
faces that fall outside of the dataset span, e.g., faces older than 40 years
old, facial expressions, and different ethnicities.
Head Reconstruction from Internet Photos
Authors: Shu Liang, Linda G. Shapiro, Ira Kemelmacher-Shlizerman
Date: 2018-09-13 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

3D face reconstruction from Internet photos has recently produced exciting
results. A person's face, e.g., Tom Hanks, can be modeled and animated in 3D
from a completely uncalibrated photo collection. Most methods, however, focus
solely on face area and mask out the rest of the head. This paper proposes that
head modeling from the Internet is a problem we can solve. We target
reconstruction of the rough shape of the head. Our method is to gradually
"grow" the head mesh starting from the frontal face and extending to the rest
of views using photometric stereo constraints. We call our method
boundary-value growing algorithm. Results on photos of celebrities downloaded
from the Internet are presented.
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
Authors: Dan Li, Dacheng Chen, Jonathan Goh, See-Kiong Ng
Date: 2018-09-13 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with
networked sensors and actuators that are targets for cyber-attacks.
Conventional detection techniques are unable to deal with the increasingly
dynamic and complex nature of the CPSs. On the other hand, the networked
sensors and actuators generate large amounts of data streams that can be
continuously monitored for intrusion events. Unsupervised machine learning
techniques can be used to model the system behaviour and classify deviant
behaviours as possible attacks. In this work, we proposed a novel Generative
Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex
networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the
multivariate time series of the sensors and actuators under normal working
conditions of a CPS. Instead of treating each sensor's and actuator's time
series independently, we model the time series of multiple sensors and
actuators in the CPS concurrently to take into account of potential latent
interactions between them. To exploit both the generator and the discriminator
of our GAN, we deployed the GAN-trained discriminator together with the
residuals between generator-reconstructed data and the actual samples to detect
possible anomalies in the complex CPS. We used our GAN-AD to distinguish
abnormal attacked situations from normal working conditions for a complex
six-stage Secure Water Treatment (SWaT) system. Experimental results showed
that the proposed strategy is effective in identifying anomalies caused by
various attacks with high detection rate and low false positive rate as
compared to existing methods.
Geodesic Clustering in Deep Generative Models
Authors: Tao Yang, Georgios Arvanitidis, Dongmei Fu, Xiaogang Li, Søren Hauberg
Date: 2018-09-13 00:00:00
Tasks: Clustering

Deep generative models are tremendously successful in learning
low-dimensional latent representations that well-describe the data. These
representations, however, tend to much distort relationships between points,
i.e. pairwise distances tend to not reflect semantic similarities well. This
renders unsupervised tasks, such as clustering, difficult when working with the
latent representations. We demonstrate that taking the geometry of the
generative model into account is sufficient to make simple clustering
algorithms work well over latent representations. Leaning on the recent finding
that deep generative models constitute stochastically immersed Riemannian
manifolds, we propose an efficient algorithm for computing geodesics (shortest
paths) and computing distances in the latent space, while taking its distortion
into account. We further propose a new architecture for modeling uncertainty in
variational autoencoders, which is essential for understanding the geometry of
deep generative models. Experiments show that the geodesic distance is very
likely to reflect the internal structure of the data.
Adversarial Feature Sampling Learning for Efficient Visual Tracking
Authors: Yingjie Yin, Lei Zhang, De Xu, Xingang Wang
Date: 2018-09-13 00:00:00
Tasks: Generative Adversarial Network, Object, Visual Tracking

The tracking-by-detection framework usually consist of two stages: drawing
samples around the target object in the first stage and classifying each sample
as the target object or background in the second stage. Current popular
trackers based on tracking-by-detection framework typically draw samples in the
raw image as the inputs of deep convolution networks in the first stage, which
usually results in high computational burden and low running speed. In this
paper, we propose a new visual tracking method using sampling deep
convolutional features to address this problem. Only one cropped image around
the target object is input into the designed deep convolution network and the
samples is sampled on the feature maps of the network by spatial bilinear
resampling. In addition, a generative adversarial network is integrated into
our network framework to augment positive samples and improve the tracking
performance. Extensive experiments on benchmark datasets demonstrate that the
proposed method achieves a comparable performance to state-of-the-art trackers
and accelerates tracking-by-detection trackers based on raw-image samples
effectively.