pwc-archive/papers-with-abstracts huggingface dataset:
Algorithms for Destructive Shift Bribery
Authors: Andrzej Kaczmarczyk, Piotr Faliszewski
Date: 2018-10-03 00:00:00

We study the complexity of Destructive Shift Bribery. In this problem, we are
given an election with a set of candidates and a set of voters (each ranking
the candidates from the best to the worst), a despised candidate $d$, a budget
$B$, and prices for shifting $d$ back in the voters' rankings. The goal is to
ensure that $d$ is not a winner of the election. We show that this problem is
polynomial-time solvable for scoring protocols (encoded in unary), the Bucklin
and Simplified Bucklin rules, and the Maximin rule, but is NP-hard for the
Copeland rule. This stands in contrast to the results for the constructive
setting (known from the literature), for which the problem is polynomial-time
solvable for $k$-Approval family of rules, but is NP-hard for the Borda,
Copeland, and Maximin rules. We complement the analysis of the Copeland rule
showing W-hardness for the parameterization by the budget value, and by the
number of affected voters. We prove that the problem is W-hard when
parameterized by the number of voters even for unit prices. From the positive
perspective we provide an efficient algorithm for solving the problem
parameterized by the combined parameter the number of candidates and the
maximum bribery price (alternatively the number of different bribery prices).
A characterization of the Edge of Criticality in Binary Echo State Networks
Authors: Pietro Verzelli, Lorenzo Livi, Cesare Alippi
Date: 2018-10-03 00:00:00

Echo State Networks (ESNs) are simplified recurrent neural network models
composed of a reservoir and a linear, trainable readout layer. The reservoir is
tunable by some hyper-parameters that control the network behaviour. ESNs are
known to be effective in solving tasks when configured on a region in
(hyper-)parameter space called \emph{Edge of Criticality} (EoC), where the
system is maximally sensitive to perturbations hence affecting its behaviour.
In this paper, we propose binary ESNs, which are architecturally equivalent to
standard ESNs but consider binary activation functions and binary recurrent
weights. For these networks, we derive a closed-form expression for the EoC in
the autonomous case and perform simulations in order to assess their behavior
in the case of noisy neurons and in the presence of a signal. We propose a
theoretical explanation for the fact that the variance of the input plays a
major role in characterizing the EoC.
A deep learning pipeline for product recognition on store shelves
Authors: Alessio Tonioni, Eugenio Serra, Luigi Di Stefano
Date: 2018-10-03 00:00:00
Tasks: Deep Learning, Image Retrieval, object-detection, Object Detection, Retrieval

Recognition of grocery products in store shelves poses peculiar challenges.
Firstly, the task mandates the recognition of an extremely high number of
different items, in the order of several thousands for medium-small shops, with
many of them featuring small inter and intra class variability. Then, available
product databases usually include just one or a few studio-quality images per
product (referred to herein as reference images), whilst at test time
recognition is performed on pictures displaying a portion of a shelf containing
several products and taken in the store by cheap cameras (referred to as query
images). Moreover, as the items on sale in a store as well as their appearance
change frequently over time, a practical recognition system should handle
seamlessly new products/packages. Inspired by recent advances in object
detection and image retrieval, we propose to leverage on state of the art
object detectors based on deep learning to obtain an initial productagnostic
item detection. Then, we pursue product recognition through a similarity search
between global descriptors computed on reference and cropped query images. To
maximize performance, we learn an ad-hoc global descriptor by a CNN trained on
reference images based on an image embedding loss. Our system is
computationally expensive at training time but can perform recognition rapidly
and accurately at test time.
2018 Low-Power Image Recognition Challenge
Authors: Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
Date: 2018-10-03 00:00:00

The Low-Power Image Recognition Challenge (LPIRC,
https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in
2015. The competition identifies the best technologies that can classify and
detect objects in images efficiently (short execution time and low energy
consumption) and accurately (high precision). Over the four years, the winners'
scores have improved more than 24 times. As computer vision is widely used in
many battery-powered systems (such as drones and mobile phones), the need for
low-power computer vision will become increasingly important. This paper
summarizes LPIRC 2018 by describing the three different tracks and the winners'
solutions.
Can everyday AI be ethical. Fairness of Machine Learning Algorithms
Authors: Philippe Besse, Celine Castets-Renard, Aurelien Garivier, Jean-Michel Loubes
Date: 2018-10-03 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Combining big data and machine learning algorithms, the power of automatic
decision tools induces as much hope as fear. Many recently enacted European
legislation (GDPR) and French laws attempt to regulate the use of these tools.
Leaving aside the well-identified problems of data confidentiality and
impediments to competition, we focus on the risks of discrimination, the
problems of transparency and the quality of algorithmic decisions. The detailed
perspective of the legal texts, faced with the complexity and opacity of the
learning algorithms, reveals the need for important technological disruptions
for the detection or reduction of the discrimination risk, and for addressing
the right to obtain an explanation of the auto- matic decision. Since trust of
the developers and above all of the users (citizens, litigants, customers) is
essential, algorithms exploiting personal data must be deployed in a strict
ethical framework. In conclusion, to answer this need, we list some ways of
controls to be developed: institutional control, ethical charter, external
audit attached to the issue of a label.
A Nonparametric Approach to High-dimensional k-sample Comparison Problems
Authors: Subhadeep, Mukhopadhyay, Kaijun Wang
Date: 2018-10-03 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-dimensional k-sample comparison is a common applied problem. We construct a class of easy-to-implement nonparametric distribution-free tests based on new tools and unexplored connections with spectral graph theory. The test is shown to possess various desirable properties along with a characteristic exploratory flavor that has practical consequences. The numerical examples show that our method works surprisingly well under a broad range of realistic situations.
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
Authors: Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb
Date: 2018-09-11 00:00:00
Tasks: 4k, Relational Reasoning

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.
Sum decomposition of divergence into three divergences
Authors: Tomohiro Nishiyama
Date: 2018-10-03 00:00:00
Tasks: BIG-bench Machine Learning

Divergence functions play a key role as to measure the discrepancy between
two points in the field of machine learning, statistics and signal processing.
Well-known divergences are the Bregman divergences, the Jensen divergences and
the f-divergences. In this paper, we show that the symmetric Bregman divergence
can be decomposed into the sum of two types of Jensen divergences and the
Bregman divergence. Furthermore, applying this result, we show another sum
decomposition of divergence is possible which includes f-divergences
explicitly.
Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening
Authors: Hiroki Kato, Hiroyuki Hanada, Ichiro Takeuchi
Date: 2018-10-03 00:00:00
Tasks: All, Prediction

We consider the problem of learning a sparse rule model, a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyper-rectangle in the input space. Since the number of all possible such rules is extremely large, it has been computationally intractable to select the optimal set of active rules. In this paper, to solve this difficulty for learning the optimal sparse rule model, we propose Safe RuleFit (SRF). Our basic idea is to develop meta safe screening (mSS), which is a non-trivial extension of well-known safe screening (SS) techniques. While SS is used for screening out one feature, mSS can be used for screening out multiple features by exploiting the inclusion-relations of hyper-rectangles in the input space. SRF provides a general framework for fitting sparse rule models for regression and classification, and it can be extended to handle more general sparse regularizations such as group regularization. We demonstrate the advantages of SRF through intensive numerical experiments.
A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation
Authors: Lukas Hoyer, Christoph Steup, Sanaz Mostaghim
Date: 2018-10-03 00:00:00
Tasks: GPU, object-detection, Object Detection, Pose Estimation

External localization is an essential part for the indoor operation of small
or cost-efficient robots, as they are used, for example, in swarm robotics. We
introduce a two-stage localization and instance identification framework for
arbitrary robots based on convolutional neural networks. Object detection is
performed on an external camera image of the operation zone providing robot
bounding boxes for an identification and orientation estimation convolutional
neural network. Additionally, we propose a process to generate the necessary
training data. The framework was evaluated with 3 different robot types and
various identification patterns. We have analyzed the main framework
hyperparameters providing recommendations for the framework operation settings.
We achieved up to 98% mAP@IOU0.5 and only 1.6{\deg} orientation error, running
with a frame rate of 50 Hz on a GPU.
A Comparative Study of Neural Network Models for Sentence Classification
Authors: Phuong Le-Hong, Anh-Cuong Le
Date: 2018-10-03 00:00:00
Tasks: Feature Engineering, General Classification, Sentence, Sentence Classification, Word Embeddings

This paper presents an extensive comparative study of four neural network
models, including feed-forward networks, convolutional networks, recurrent
networks and long short-term memory networks, on two sentence classification
datasets of English and Vietnamese text. We show that on the English dataset,
the convolutional network models without any feature engineering outperform
some competitive sentence classifiers with rich hand-crafted linguistic
features. We demonstrate that the GloVe word embeddings are consistently better
than both Skip-gram word embeddings and word count vectors. We also show the
superiority of convolutional neural network models on a Vietnamese newspaper
sentence dataset over strong baseline models. Our experimental results suggest
some good practices for applying neural network models in sentence
classification.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Authors: Andrey Ignatov, Radu Timofte, Thang Van Vu, Tung Minh Luu, Trung X. Pham, Cao Van Nguyen, Yongwoo Kim, Jae-Seok Choi, Munchurl Kim, Jie Huang, Jiewen Ran, Chen Xing, Xingguang Zhou, Pengfei Zhu, Mingrui Geng, Yawei Li, Eirikur Agustsson, Shuhang Gu, Luc van Gool, Etienne de Stoutz, Nikolay Kobyshev, Kehui Nie, Yan Zhao, Gen Li, Tong Tong, Qinquan Gao, Liu Hanwen, Pablo Navarrete Michelini, Zhu Dan, Hu Fengshuo, Zheng Hui, Xiumei Wang, Lirui Deng, Rang Meng, Jinghui Qin, Yukai Shi, Wushao Wen, Liang Lin, Ruicheng Feng, Shixiang Wu, Chao Dong, Yu Qiao, Subeesh Vasu, Nimisha Thekke Madam, Praveen Kandula, A. N. Rajagopalan, Jie Liu, Cheolkon Jung
Date: 2018-10-03 00:00:00
Tasks: Image Enhancement, Image Super-Resolution, Super-Resolution

This paper reviews the first challenge on efficient perceptual image
enhancement with the focus on deploying deep learning models on smartphones.
The challenge consisted of two tracks. In the first one, participants were
solving the classical image super-resolution problem with a bicubic downscaling
factor of 4. The second track was aimed at real-world photo enhancement, and
the goal was to map low-quality photos from the iPhone 3GS device to the same
photos captured with a DSLR camera. The target metric used in this challenge
combined the runtime, PSNR scores and solutions' perceptual results measured in
the user study. To ensure the efficiency of the submitted models, we
additionally measured their runtime and memory requirements on Android
smartphones. The proposed solutions significantly improved baseline results
defining the state-of-the-art for image enhancement on smartphones.
Optimization Algorithm Inspired Deep Neural Network Structure Design
Authors: Huan Li, Yibo Yang, Dongmin Chen, Zhouchen Lin
Date: 2018-10-03 00:00:00

Deep neural networks have been one of the dominant machine learning
approaches in recent years. Several new network structures are proposed and
have better performance than the traditional feedforward neural network
structure. Representative ones include the skip connection structure in ResNet
and the dense connection structure in DenseNet. However, it still lacks a
unified guidance for the neural network structure design. In this paper, we
propose the hypothesis that the neural network structure design can be inspired
by optimization algorithms and a faster optimization algorithm may lead to a
better neural network structure. Specifically, we prove that the propagation in
the feedforward neural network with the same linear transformation in different
layers is equivalent to minimizing some function using the gradient descent
algorithm. Based on this observation, we replace the gradient descent algorithm
with the heavy ball algorithm and Nesterov's accelerated gradient descent
algorithm, which are faster and inspire us to design new and better network
structures. ResNet and DenseNet can be considered as two special cases of our
framework. Numerical experiments on CIFAR-10, CIFAR-100 and ImageNet verify the
advantage of our optimization algorithm inspired structures over ResNet and
DenseNet.
Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control
Authors: Wendi Xu, Ming Zhang
Date: 2018-10-03 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

Despite its remarkable empirical success as a highly competitive branch of
artificial intelligence, deep learning is often blamed for its widely known low
interpretation and lack of firm and rigorous mathematical foundation. However,
most theoretical endeavor is devoted in discriminative deep learning case,
whose complementary part is generative deep learning. To the best of our
knowledge, we firstly highlight landscape of empirical error in generative case
to complete the full picture through exquisite design of image super resolution
under norm based capacity control. Our theoretical advance in interpretation of
the training dynamic is achieved from both mathematical and biological sides.
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?
Authors: Bilwaj Gaonkar, Matthew Edwards, Alex Bui, Matthew Brown, Luke Macyszyn
Date: 2018-10-03 00:00:00
Tasks: Data Augmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Yes, it can. Data augmentation is perhaps the oldest preprocessing step in computer vision literature. Almost every computer vision model trained on imaging data uses some form of augmentation. In this paper, we use the inter-vertebral disk segmentation task alongside a deep residual U-Net as the learning model, to explore the effectiveness of augmentation. In the extreme, we observed that a model trained on patches extracted from just one scan, with each patch augmented 50 times; achieved a Dice score of 0.73 in a validation set of 40 cases. Qualitative evaluation indicated a clinically usable segmentation algorithm, which appropriately segments regions of interest, alongside limited false positive specks. When the initial patches are extracted from nine scans the average Dice coefficient jumps to 0.86 and most of the false positives disappear. While this still falls short of state-of-the-art deep learning based segmentation of discs reported in literature, qualitative examination reveals that it does yield segmentation, which can be amended by expert clinicians with minimal effort to generate additional data for training improved deep models. Extreme augmentation of training data, should thus be construed as a strategy for training deep learning based algorithms, when very little manually annotated data is available to work with. Models trained with extreme augmentation can then be used to accelerate the generation of manually labelled data. Hence, we show that extreme augmentation can be a valuable tool in addressing scaling up small imaging data sets to address medical image segmentation tasks.
Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution
Authors: Wendi Xu, Ming Zhang
Date: 2018-10-03 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

Evolution of deep learning shows that some algorithmic tricks are more
durable , while others are not. To the best of our knowledge, we firstly
summarize 5 more durable and complete deep learning components for vision, that
is, WARSHIP. Moreover, we give a biological overview of WARSHIP, emphasizing
brain-inspired computing of WARSHIP. As a step towards WARSHIP, our case study
of image super resolution combines 3 components of RSH to deploy a CNN model of
WARSHIP-XZNet, which performs a happy medium between speed and performance.
Cascaded Pyramid Network for 3D Human Pose Estimation Challenge
Authors: Sungeun Hong, Wonjin Jung, Ilsang Woo, Seung Wook Kim
Date: 2018-10-03 00:00:00
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation, Position

Over the past decade, there has been a growing interest in human pose
estimation. Although much work has been done on 2D pose estimation, 3D pose
estimation has still been relatively studied less. In this paper, we propose a
top-bottom based two-stage 3D estimation framework. GloabalNet and RefineNet in
our 2D pose estimation process enable us to find occluded or invisible 2D
joints while 2D-to-3D pose estimator composed of residual blocks is used to
lift 2D joints to 3D joints effectively. The proposed method achieves promising
results with mean per joint position error at 42.39 on the validation dataset
on `3D Human Pose Estimation within the ECCV 2018 PoseTrack Challenge.'
Primitive Fitting Using Deep Boundary Aware Geometric Segmentation
Authors: Duanshun Li, Chen Feng
Date: 2018-10-03 00:00:00
Tasks: Semantic Segmentation

To identify and fit geometric primitives (e.g., planes, spheres, cylinders,
cones) in a noisy point cloud is a challenging yet beneficial task for fields
such as robotics and reverse engineering. As a multi-model multi-instance
fitting problem, it has been tackled with different approaches including
RANSAC, which however often fit inferior models in practice with noisy inputs
of cluttered scenes. Inspired by the corresponding human recognition process,
and benefiting from the recent advancements in image semantic segmentation
using deep neural networks, we propose BAGSFit as a new framework addressing
this problem. Firstly, through a fully convolutional neural network, the input
point cloud is point-wisely segmented into multiple classes divided by jointly
detected instance boundaries without any geometric fitting. Thus, segments can
serve as primitive hypotheses with a probability estimation of associating
primitive classes. Finally, all hypotheses are sent through a geometric
verification to correct any misclassification by fitting primitives
respectively. We performed training using simulated range images and tested it
with both simulated and real-world point clouds. Quantitative and qualitative
experiments demonstrated the superiority of BAGSFit.
Interpreting Layered Neural Networks via Hierarchical Modular Representation
Authors: Chihiro Watanabe
Date: 2018-10-03 00:00:00
Tasks: Clustering

Interpreting the prediction mechanism of complex models is currently one of
the most important tasks in the machine learning field, especially with layered
neural networks, which have achieved high predictive performance with various
practical data sets. To reveal the global structure of a trained neural network
in an interpretable way, a series of clustering methods have been proposed,
which decompose the units into clusters according to the similarity of their
inference roles. The main problems in these studies were that (1) we have no
prior knowledge about the optimal resolution for the decomposition, or the
appropriate number of clusters, and (2) there was no method with which to
acquire knowledge about whether the outputs of each cluster have a positive or
negative correlation with the input and output dimension values. In this paper,
to solve these problems, we propose a method for obtaining a hierarchical
modular representation of a layered neural network. The application of a
hierarchical clustering method to a trained network reveals a tree-structured
relationship among hidden layer units, based on their feature vectors defined
by their correlation with the input and output dimension values.