pwc-archive/papers-with-abstracts huggingface dataset:
A formal framework for deliberated judgment
Authors: Olivier Cailloux, Yves Meinard
Date: 2018-01-17 00:00:00
Tasks: valid

While the philosophical literature has extensively studied how decisions relate to arguments, reasons and justifications, decision theory almost entirely ignores the latter notions and rather focuses on preference and belief. In this article, we argue that decision theory can largely benefit from explicitly taking into account the stance that decision-makers take towards arguments and counter-arguments. To that end, we elaborate a formal framework aiming to integrate the role of arguments and argumentation in decision theory and decision aid. We start from a decision situation, where an individual requests decision support. In this context, we formally define, as a commendable basis for decision-aid, this individual's deliberated judgment, popularized by Rawls. We explain how models of deliberated judgment can be validated empirically. We then identify conditions upon which the existence of a valid model can be taken for granted, and analyze how these conditions can be relaxed. We then explore the significance of our proposed framework for decision aiding practice. We argue that our concept of deliberated judgment owes its normative credentials both to its normative foundations (the idea of rationality based on arguments) and to its reference to empirical reality (the stance that real, empirical individuals hold towards arguments and counter-arguments, on due reflection). We then highlight that our framework opens promising avenues for future research involving both philosophical and decision theoretic approaches, as well as empirical implementations.
The Case for Automatic Database Administration using Deep Reinforcement Learning
Authors: Ankur Sharma, Felix Martin Schuhknecht, Jens Dittrich
Date: 2018-01-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Like any large software system, a full-fledged DBMS offers an overwhelming
amount of configuration knobs. These range from static initialisation
parameters like buffer sizes, degree of concurrency, or level of replication to
complex runtime decisions like creating a secondary index on a particular
column or reorganising the physical layout of the store. To simplify the
configuration, industry grade DBMSs are usually shipped with various advisory
tools, that provide recommendations for given workloads and machines. However,
reality shows that the actual configuration, tuning, and maintenance is usually
still done by a human administrator, relying on intuition and experience.
Recent work on deep reinforcement learning has shown very promising results in
solving problems, that require such a sense of intuition. For instance, it has
been applied very successfully in learning how to play complicated games with
enormous search spaces. Motivated by these achievements, in this work we
explore how deep reinforcement learning can be used to administer a DBMS.
First, we will describe how deep reinforcement learning can be used to
automatically tune an arbitrary software system like a DBMS by defining a
problem environment. Second, we showcase our concept of NoDBA at the concrete
example of index selection and evaluate how well it recommends indexes for
given workloads.
Discriminative Correlation Filter with Channel and Spatial Reliability
Authors: Alan Lukežič, Tomáš Vojíř, Luka Čehovin, Jiří Matas, Matej Kristan
Date: 2016-11-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: CPU, Visual Object Tracking

Short-term tracking is an open and challenging problem for which
discriminative correlation filters (DCF) have shown excellent performance. We
introduce the channel and spatial reliability concepts to DCF tracking and
provide a novel learning algorithm for its efficient and seamless integration
in the filter update and the tracking process. The spatial reliability map
adjusts the filter support to the part of the object suitable for tracking.
This both allows to enlarge the search region and improves tracking of
non-rectangular objects. Reliability scores reflect channel-wise quality of the
learned filters and are used as feature weighting coefficients in localization.
Experimentally, with only two simple standard features, HoGs and Colornames,
the novel CSR-DCF method -- DCF with Channel and Spatial Reliability --
achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF
runs in real-time on a CPU.
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
Authors: Pavel Izmailov, Alexander Novikov, Dmitry Kropotov
Date: 2017-10-19 00:00:00
Tasks: Gaussian Processes, Variational Inference

We propose a method (TT-GP) for approximate inference in Gaussian Process
(GP) models. We build on previous scalable GP research including stochastic
variational inference based on inducing inputs, kernel interpolation, and
structure exploiting algebra. The key idea of our method is to use Tensor Train
decomposition for variational parameters, which allows us to train GPs with
billions of inducing inputs and achieve state-of-the-art results on several
benchmarks. Further, our approach allows for training kernels based on deep
neural networks without any modifications to the underlying GP model. A neural
network learns a multidimensional embedding for the data, which is used by the
GP to make the final prediction. We train GP and neural network parameters
end-to-end without pretraining, through maximization of GP marginal likelihood.
We show the efficiency of the proposed approach on several regression and
classification benchmark datasets including MNIST, CIFAR-10, and Airline.
Automatic Detection of Cyberbullying in Social Media Text
Authors: Cynthia Van Hee, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter Daelemans, Véronique Hoste
Date: 2018-01-17 00:00:00
Tasks: Binary Classification

While social media offer great communication opportunities, they also
increase the vulnerability of young people to threatening situations online.
Recent studies report that cyberbullying constitutes a growing problem among
youngsters. Successful prevention depends on the adequate detection of
potentially harmful messages and the information overload on the Web requires
intelligent systems to identify potential risks automatically. The focus of
this paper is on automatic cyberbullying detection in social media text by
modelling posts written by bullies, victims, and bystanders of online bullying.
We describe the collection and fine-grained annotation of a training corpus for
English and Dutch and perform a series of binary classification experiments to
determine the feasibility of automatic cyberbullying detection. We make use of
linear support vector machines exploiting a rich feature set and investigate
which information sources contribute the most for this particular task.
Experiments on a holdout test set reveal promising results for the detection of
cyberbullying-related posts. After optimisation of the hyperparameters, the
classifier yields an F1-score of 64% and 61% for English and Dutch
respectively, and considerably outperforms baseline systems based on keywords
and word unigrams.
FastNet
Authors: John Olafenwa, Moses Olafenwa
Date: 2018-01-17 00:00:00
Tasks: Computational Efficiency

Inception and the Resnet family of Convolutional Neural Network
archi-tectures have broken records in the past few years, but recent state of
the art models have also incurred very high computational cost in terms of
training, inference and model size. Making the deployment of these models on
Edge devices, impractical. In light of this, we present a new novel
architecture that is designed for high computational efficiency on both GPUs
and CPUs, and is highly suited for deployment on Mobile Applications, Smart
Cameras, Iot devices and controllers as well as low cost drones. Our
architecture boasts competitive accuracies on standard Datasets even
out-performing the original Resnet. We present below the motivation for this
research, the architecture of the network, single test accuracies on CIFAR 10
and CIFAR 100 , a detailed comparison with other well-known architectures and
link to an implementation in Keras.
Unseen Class Discovery in Open-world Classification
Authors: Lei Shu, Hu Xu, Bing Liu
Date: 2018-01-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, Clustering, General Classification

This paper concerns open-world classification, where the classifier not only
needs to classify test examples into seen classes that have appeared in
training but also reject examples from unseen or novel classes that have not
appeared in training. Specifically, this paper focuses on discovering the
hidden unseen classes of the rejected examples. Clearly, without prior
knowledge this is difficult. However, we do have the data from the seen
training classes, which can tell us what kind of similarity/difference is
expected for examples from the same class or from different classes. It is
reasonable to assume that this knowledge can be transferred to the rejected
examples and used to discover the hidden unseen classes in them. This paper
aims to solve this problem. It first proposes a joint open classification model
with a sub-model for classifying whether a pair of examples belongs to the same
or different classes. This sub-model can serve as a distance function for
clustering to discover the hidden classes of the rejected examples.
Experimental results show that the proposed model is highly promising.
Multi-View Stereo 3D Edge Reconstruction
Authors: Andrea Bignoli, Andrea Romanoni, Matteo Matteucci
Date: 2018-01-17 00:00:00

This paper presents a novel method for the reconstruction of 3D edges in
multi-view stereo scenarios. Previous research in the field typically relied on
video sequences and limited the reconstruction process to either straight
line-segments, or edge-points, i.e., 3D points that correspond to image edges.
We instead propose a system, denoted as EdgeGraph3D, able to recover both
straight and curved 3D edges from an unordered image sequence. A second
contribution of this work is a graph-based representation for 2D edges that
allows the identification of the most structurally significant edges detected
in an image. We integrate EdgeGraph3D in a multi-view stereo reconstruction
pipeline and analyze the benefits provided by 3D edges to the accuracy of the
recovered surfaces. We evaluate the effectiveness of our approach on multiple
datasets from two different collections in the multi-view stereo literature.
Experimental results demonstrate the ability of EdgeGraph3D to work in presence
of strong illumination changes and reflections, which are usually detrimental
to the effectiveness of classical photometric reconstruction systems.
Graph Embedding with Rich Information through Heterogeneous Network
Authors: Guolei Sun, Xiangliang Zhang
Date: 2017-10-18 00:00:00
Tasks: General Classification, Graph Embedding, Node Classification, Representation Learning

Graph embedding has attracted increasing attention due to its critical
application in social network analysis. Most existing algorithms for graph
embedding only rely on the typology information and fail to use the copious
information in nodes as well as edges. As a result, their performance for many
tasks may not be satisfactory. In this paper, we proposed a novel and general
framework of representation learning for graph with rich text information
through constructing a bipartite heterogeneous network. Specially, we designed
a biased random walk to explore the constructed heterogeneous network with the
notion of flexible neighborhood. The efficacy of our method is demonstrated by
extensive comparison experiments with several baselines on various datasets. It
improves the Micro-F1 and Macro-F1 of node classification by 10% and 7% on Cora
dataset.
On the Proximal Gradient Algorithm with Alternated Inertia
Authors: Franck Iutzeler, Jerome Malick
Date: 2018-01-17 00:00:00

In this paper, we investigate the attractive properties of the proximal
gradient algorithm with inertia. Notably, we show that using alternated inertia
yields monotonically decreasing functional values, which contrasts with usual
accelerated proximal gradient methods. We also provide convergence rates for
the algorithm with alternated inertia based on local geometric properties of
the objective function. The results are put into perspective by discussions on
several extensions and illustrations on common regularized problems.
Light-weight pixel context encoders for image inpainting
Authors: Nanne van Noord, Eric Postma
Date: 2018-01-17 00:00:00
Tasks: Image Inpainting

In this work we propose Pixel Content Encoders (PCE), a light-weight image
inpainting model, capable of generating novel con-tent for large missing
regions in images. Unlike previously presented convolutional neural network
based models, our PCE model has an order of magnitude fewer trainable
parameters. Moreover, by incorporating dilated convolutions we are able to
preserve fine grained spatial information, achieving state-of-the-art
performance on benchmark datasets of natural images and paintings. Besides
image inpainting, we show that without changing the architecture, PCE can be
used for image extrapolation, generating novel content beyond existing image
boundaries.
Saliency Driven Image Manipulation
Authors: Roey Mechrez, Eli Shechtman, Lihi Zelnik-Manor
Date: 2016-12-07 00:00:00
Tasks: Image Manipulation

Have you ever taken a picture only to find out that an unimportant background
object ended up being overly salient? Or one of those team sports photos where
your favorite player blends with the rest? Wouldn't it be nice if you could
tweak these pictures just a little bit so that the distractor would be
attenuated and your favorite player will stand-out among her peers?
Manipulating images in order to control the saliency of objects is the goal of
this paper. We propose an approach that considers the internal color and
saliency properties of the image. It changes the saliency map via an
optimization framework that relies on patch-based manipulation using only
patches from within the same image to achieve realistic looking results.
Applications include object enhancement, distractors attenuation and background
decluttering. Comparing our method to previous ones shows significant
improvement, both in the achieved saliency manipulation and in the realistic
appearance of the resulting images.
Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
Authors: Ying Lu, Liming Chen, Alexandre Saidi, Xianfeng GU
Date: 2018-01-17 00:00:00
Tasks: BIG-bench Machine Learning

Correctly estimating the discrepancy between two data distributions has
always been an important task in Machine Learning. Recently, Cuturi proposed
the Sinkhorn distance which makes use of an approximate Optimal Transport cost
between two distributions as a distance to describe distribution discrepancy.
Although it has been successfully adopted in various machine learning
applications (e.g. in Natural Language Processing and Computer Vision) since
then, the Sinkhorn distance also suffers from two unnegligible limitations. The
first one is that the Sinkhorn distance only gives an approximation of the real
Wasserstein distance, the second one is the `divide by zero' problem which
often occurs during matrix scaling when setting the entropy regularization
coefficient to a small value. In this paper, we introduce a new Brenier
approach for calculating a more accurate Wasserstein distance between two
discrete distributions, this approach successfully avoids the two limitations
shown above for Sinkhorn distance and gives an alternative way for estimating
distribution discrepancy.
Image Captioning using Deep Neural Architectures
Authors: Parth Shah, Vishvajit Bakarola, Supriya Pati
Date: 2018-01-17 00:00:00
Tasks: Image Captioning, Machine Translation, Object Recognition, Sentence, Translation

Automatically creating the description of an image using any natural
languages sentence like English is a very challenging task. It requires
expertise of both image processing as well as natural language processing. This
paper discuss about different available models for image captioning task. We
have also discussed about how the advancement in the task of object recognition
and machine translation has greatly improved the performance of image
captioning model in recent years. In addition to that we have discussed how
this model can be implemented. In the end, we have also evaluated the
performance of model using standard evaluation matrices.
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
Authors: Jiaming Song, Yuhuai Wu
Date: 2018-01-17 00:00:00
Tasks: MuJoCo, Sensitivity

In this technical report, we consider an approach that combines the PPO
objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
We perform a range of empirical analysis on various aspects of the algorithm,
such as sample complexity, training speed, and sensitivity to batch size and
training epochs. We observe that PPOKFAC is able to outperform PPO in terms of
sample complexity and speed in a range of MuJoCo environments, while being
scalable in terms of batch size. In spite of this, it seems that adding more
epochs is not necessarily helpful for sample efficiency, and PPOKFAC seems to
be worse than its A2C counterpart, ACKTR.
Fruit Quantity and Quality Estimation using a Robotic Vision System
Authors: M. Halstead, C. McCool, S. Denman, T. Perez, C. Fookes
Date: 2018-01-17 00:00:00

Accurate localisation of crop remains highly challenging in unstructured
environments such as farms. Many of the developed systems still rely on the use
of hand selected features for crop identification and often neglect the
estimation of crop quantity and quality, which is key to assigning labor during
farming processes. To alleviate these limitations we present a robotic vision
system that can accurately estimate the quantity and quality of sweet pepper
(Capsicum annuum L), a key horticultural crop. This system consists of three
parts: detection, quality estimation, and tracking. Efficient detection is
achieved using the FasterRCNN framework. Quality is then estimated in the same
framework by learning a parallel layer which we show experimentally results in
superior performance than treating quality as extra classes in the traditional
Faster-RCNN framework. Evaluation of these two techniques outlines the improved
performance of the parallel layer, where we achieve an F1 score of 77.3 for the
parallel technique yet only 72.5 for the best scoring (red) of the multi-class
implementation. To track the crop we present a tracking via detection approach,
which uses the FasterRCNN with parallel layers, that is also a vision-only
solution. This approach is cheap to implement as it only requires a camera and
in experiments across 2 days we show that our proposed system can accurately
estimate the number of sweet pepper present, within 4.1% of the ground truth.
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Authors: Yanning Shen, Tianyi Chen, Georgios B. Giannakis
Date: 2017-12-28 00:00:00

Kernel-based methods exhibit well-documented performance in various nonlinear
learning tasks. Most of them rely on a preselected kernel, whose prudent choice
presumes task-specific prior information. Especially when the latter is not
available, multi-kernel learning has gained popularity thanks to its
flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging
the random feature approximation and its recent orthogonality-promoting
variant, the present contribution develops a scalable multi-kernel learning
scheme (termed Raker) to obtain the sought nonlinear learning function `on the
fly,' first for static environments. To further boost performance in dynamic
environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is
developed. AdaRaker accounts not only for data-driven learning of kernel
combination, but also for the unknown dynamics. Performance is analyzed in
terms of both static and dynamic regrets. AdaRaker is uniquely capable of
tracking nonlinear learning functions in environments with unknown dynamics,
and with with analytic performance guarantees. Tests with synthetic and real
datasets are carried out to showcase the effectiveness of the novel algorithms.
Semi-supervised FusedGAN for Conditional Image Generation
Authors: Navaneeth Bodla, Gang Hua, Rama Chellappa
Date: 2018-01-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Conditional Image Generation, Diversity, Face Generation, Image Generation, Unconditional Image Generation

We present FusedGAN, a deep network for conditional image synthesis with
controllable sampling of diverse images. Fidelity, diversity and controllable
sampling are the main quality measures of a good image generation model. Most
existing models are insufficient in all three aspects. The FusedGAN can perform
controllable sampling of diverse images with very high fidelity. We argue that
controllability can be achieved by disentangling the generation process into
various stages. In contrast to stacked GANs, where multiple stages of GANs are
trained separately with full supervision of labeled intermediate images, the
FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike
existing methods, which requires full supervision with paired conditions and
images, the FusedGAN can effectively leverage more abundant images without
corresponding conditions in training, to produce more diverse samples with high
fidelity. We achieve this by fusing two generators: one for unconditional image
generation, and the other for conditional image generation, where the two
partly share a common latent space thereby disentangling the generation. We
demonstrate the efficacy of the FusedGAN in fine grained image generation tasks
such as text-to-image, and attribute-to-face generation.
RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Authors: Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas
Date: 2018-01-17 00:00:00
Tasks: Decoder, Face Alignment

We propose a novel method for real-time face alignment in videos based on a
recurrent encoder-decoder network model. Our proposed model predicts 2D facial
point heat maps regularized by both detection and regression loss, while
uniquely exploiting recurrent learning at both spatial and temporal dimensions.
At the spatial level, we add a feedback loop connection between the combined
output response map and the input, in order to enable iterative coarse-to-fine
face alignment using a single network model, instead of relying on traditional
cascaded model ensembles. At the temporal level, we first decouple the features
in the bottleneck of the network into temporal-variant factors, such as pose
and expression, and temporal-invariant factors, such as identity information.
Temporal recurrent learning is then applied to the decoupled temporal-variant
features. We show that such feature disentangling yields better generalization
and significantly more accurate results at test time. We perform a
comprehensive experimental analysis, showing the importance of each component
of our proposed model, as well as superior results over the state of the art
and several variations of our method in standard datasets.