pwc-archive/papers-with-abstracts huggingface dataset:
Application-driven Privacy-preserving Data Publishing with Correlated Attributes
Authors: Aria Rezaei, Chaowei Xiao, Jie Gao, Bo Li, Sirajum Munir
Date: 2018-12-26 00:00:00
Tasks: Privacy Preserving

Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes -- which may be hidden and can be inferred by machine learning algorithms -- while preserving the data utility in the target application. Unlike prior works, the public's possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects' identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks
Authors: Sukesh Adiga V, Jayanthi Sivaswamy
Date: 2018-12-26 00:00:00
Tasks: Denoising, Image Denoising

Fingerprint is a common biometric used for authentication and verification of
an individual. These images are degraded when fingers are wet, dirty, dry or
wounded and due to the failure of the sensors, etc. The extraction of the
fingerprint from a degraded image requires denoising and inpainting. We propose
to address these problems with an end-to-end trainable Convolutional Neural
Network based architecture called FPD-M-net, by posing the fingerprint
denoising and inpainting problem as a segmentation (foreground) task. Our
architecture is based on the M-net with a change: structure similarity loss
function, used for better extraction of the fingerprint from the noisy
background. Our method outperforms the baseline method and achieves an overall
3rd rank in the Chalearn LAP Inpainting Competition Track 3 - Fingerprint
Denoising and Inpainting, ECCV 2018
Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit
Authors: Yawei Zhao, En Zhu, Xinwang Liu, Jianping Yin
Date: 2018-12-26 00:00:00

We provide a new theoretical analysis framework to investigate online
gradient descent in the dynamic environment. Comparing with the previous work,
the new framework recovers the state-of-the-art dynamic regret, but does not
require extra gradient queries for every iteration. Specifically, when
functions are $\alpha$ strongly convex and $\beta$ smooth, to achieve the
state-of-the-art dynamic regret, the previous work requires $O(\kappa)$ with
$\kappa = \frac{\beta}{\alpha}$ queries of gradients at every iteration. But,
our framework shows that the query complexity can be improved to be $O(1)$,
which does not depend on $\kappa$. The improvement is significant for
ill-conditioned problems because that their objective function usually has a
large $\kappa$.
Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning
Authors: Babak Mahdavi-Damghani, Konul Mustafayeva, Stephen Roberts, Cristin Buescu
Date: 2018-12-26 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Portfolio Optimization

With the recent rise of Machine Learning as a candidate to partially replace classic Financial Mathematics methodologies, we investigate the performances of both in solving the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two assets that are intertwined. In Financial Mathematics approach we model the asset prices not via the common approaches used in pairs trading such as a high correlation or cointegration, but with the cointelation model that aims to reconcile both short-term risk and long-term equilibrium. We maximize the overall P&L with Financial Mathematics approach that dynamically switches between a mean-variance optimal strategy and a power utility maximizing strategy. We use a stochastic control formulation of the problem of power utility maximization and solve numerically the resulting HJB equation with the Deep Galerkin method. We turn to Machine Learning for the same P&L maximization problem and use clustering analysis to devise bands, combined with in-band optimization. Although this approach is model agnostic, results obtained with data simulated from the same cointelation model as FM give an edge to ML.
Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data
Authors: Bappaditya Mandal, N. B. Puhan, Avijit Verma
Date: 2018-12-26 00:00:00
Tasks: Benchmarking, Food Recognition, Generative Adversarial Network

Traditional machine learning algorithms using hand-crafted feature extraction
techniques (such as local binary pattern) have limited accuracy because of high
variation in images of the same class (or intra-class variation) for food
recognition task. In recent works, convolutional neural networks (CNN) have
been applied to this task with better results than all previously reported
methods. However, they perform best when trained with large amount of annotated
(labeled) food images. This is problematic when obtained in large volume,
because they are expensive, laborious and impractical. Our work aims at
developing an efficient deep CNN learning-based method for food recognition
alleviating these limitations by using partially labeled training data on
generative adversarial networks (GANs). We make new enhancements to the
unsupervised training architecture introduced by Goodfellow et al. (2014),
which was originally aimed at generating new data by sampling a dataset. In
this work, we make modifications to deep convolutional GANs to make them robust
and efficient for classifying food images. Experimental results on benchmarking
datasets show the superiority of our proposed method as compared to the
current-state-of-the-art methodologies even when trained with partially labeled
training data.
Dropout Regularization in Hierarchical Mixture of Experts
Authors: Ozan İrsoy, Ethem Alpaydin
Date: 2018-12-25 00:00:00
Tasks: Mixture-of-Experts

Dropout is a very effective method in preventing overfitting and has become
the go-to regularizer for multi-layer neural networks in recent years.
Hierarchical mixture of experts is a hierarchically gated model that defines a
soft decision tree where leaves correspond to experts and decision nodes
correspond to gating models that softly choose between its children, and as
such, the model defines a soft hierarchical partitioning of the input space. In
this work, we propose a variant of dropout for hierarchical mixture of experts
that is faithful to the tree hierarchy defined by the model, as opposed to
having a flat, unitwise independent application of dropout as one has with
multi-layer perceptrons. We show that on a synthetic regression data and on
MNIST and CIFAR-10 datasets, our proposed dropout mechanism prevents
overfitting on trees with many levels improving generalization and providing
smoother fits.
Motion Selective Prediction for Video Frame Synthesis
Authors: Veronique Prinet
Date: 2018-12-25 00:00:00
Tasks: Prediction, Video Prediction

Existing conditional video prediction approaches train a network from large
databases and generalize to previously unseen data. We take the opposite
stance, and introduce a model that learns from the first frames of a given
video and extends its content and motion, to, eg, double its length. To this
end, we propose a dual network that can use in a flexible way both dynamic and
static convolutional motion kernels, to predict future frames. The construct of
our model gives us the the means to efficiently analyze its functioning and
interpret its output. We demonstrate experimentally the robustness of our
approach on challenging videos in-the-wild and show that it is competitive wrt
related baselines.
Random deep neural networks are biased towards simple functions
Authors: Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd
Date: 2018-12-25 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: General Classification, Generalization Bounds

We prove that the binary classifiers of bit strings generated by random wide deep neural networks with ReLU activation function are biased towards simple functions. The simplicity is captured by the following two properties. For any given input bit string, the average Hamming distance of the closest input bit string with a different classification is at least sqrt(n / (2{\pi} log n)), where n is the length of the string. Moreover, if the bits of the initial string are flipped randomly, the average number of flips required to change the classification grows linearly with n. These results are confirmed by numerical experiments on deep neural networks with two hidden layers, and settle the conjecture stating that random deep neural networks are biased towards simple functions. This conjecture was proposed and numerically explored in [Valle P\'erez et al., ICLR 2019] to explain the unreasonably good generalization properties of deep learning algorithms. The probability distribution of the functions generated by random deep neural networks is a good choice for the prior probability distribution in the PAC-Bayesian generalization bounds. Our results constitute a fundamental step forward in the characterization of this distribution, therefore contributing to the understanding of the generalization properties of deep learning algorithms.
Can rationality be measured?
Authors: Tshilidzi Marwala
Date: 2018-12-25 00:00:00
Tasks: Decision Making

This paper studies whether rationality can be computed. Rationality is
defined as the use of complete information, which is processed with a perfect
biological or physical brain, in an optimized fashion. To compute rationality
one needs to quantify how complete is the information, how perfect is the
physical or biological brain and how optimized is the entire decision making
system. The rationality of a model (i.e. physical or biological brain) is
measured by the expected accuracy of the model. The rationality of the
optimization procedure is measured as the ratio of the achieved objective (i.e.
utility) to the global objective. The overall rationality of a decision is
measured as the product of the rationality of the model and the rationality of
the optimization procedure. The conclusion reached is that rationality can be
computed for convex optimization problems.
Mixed-Order Spectral Clustering for Networks
Authors: Yan Ge, Haiping Lu, Pan Peng
Date: 2018-12-25 00:00:00
Tasks: Clustering

Clustering is fundamental for gaining insights from complex networks, and
spectral clustering (SC) is a popular approach. Conventional SC focuses on
second-order structures (e.g., edges connecting two nodes) without direct
consideration of higher-order structures (e.g., triangles and cliques). This
has motivated SC extensions that directly consider higher-order structures.
However, both approaches are limited to considering a single order. This paper
proposes a new Mixed-Order Spectral Clustering (MOSC) approach to model both
second-order and third-order structures simultaneously, with two MOSC methods
developed based on Graph Laplacian (GL) and Random Walks (RW). MOSC-GL combines
edge and triangle adjacency matrices, with theoretical performance guarantee.
MOSC-RW combines first-order and second-order random walks for a probabilistic
interpretation. We automatically determine the mixing parameter based on cut
criteria or triangle density, and construct new structure-aware error metrics
for performance evaluation. Experiments on real-world networks show 1) the
superior performance of two MOSC methods over existing SC methods, 2) the
effectiveness of the mixing parameter determination strategy, and 3) insights
offered by the structure-aware error metrics.
Sequence to Sequence Learning for Query Expansion
Authors: Salah Zaiem, Fatiha Sadat
Date: 2018-12-25 00:00:00
Tasks: Information Retrieval, Keyword Extraction, Retrieval, Sentence, Sentence Embeddings

Using sequence to sequence algorithms for query expansion has not been
explored yet in Information Retrieval literature nor in Question-Answering's.
We tried to fill this gap in the literature with a custom Query Expansion
engine trained and tested on open datasets. Starting from open datasets, we
built a Query Expansion training set using sentence-embeddings-based Keyword
Extraction. We therefore assessed the ability of the Sequence to Sequence
neural networks to capture expanding relations in the words embeddings' space.
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
Authors: Lingchen Zhao, Qian Wang, Qin Zou, Yan Zhang, Yanjiao Chen
Date: 2018-12-25 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Machine Translation, Privacy Preserving, speech-recognition, Speech Recognition

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $\epsilon$-differential privacy. In particular, for the first time, we consider the existence of~\textit{unreliable participants}, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.
A Survey on Non-rigid 3D Shape Analysis
Authors: Hamid Laga
Date: 2018-12-25 00:00:00
Tasks: Medical Image Analysis, Retrieval, Survey

Shape is an important physical property of natural and manmade 3D objects
that characterizes their external appearances. Understanding differences
between shapes and modeling the variability within and across shape classes,
hereinafter referred to as \emph{shape analysis}, are fundamental problems to
many applications, ranging from computer vision and computer graphics to
biology and medicine. This chapter provides an overview of some of the recent
techniques that studied the shape of 3D objects that undergo non-rigid
deformations including bending and stretching. Recent surveys that covered some
aspects such classification, retrieval, recognition, and rigid or nonrigid
registration, focused on methods that use shape descriptors. Descriptors,
however, provide abstract representations that do not enable the exploration of
shape variability. In this chapter, we focus on recent techniques that treated
the shape of 3D objects as points in some high dimensional space where paths
describe deformations. Equipping the space with a suitable metric enables the
quantification of the range of deformations of a given shape, which in turn
enables (1) comparing and classifying 3D objects based on their shape, (2)
computing smooth deformations, i.e. geodesics, between pairs of objects, and
(3) modeling and exploring continuous shape variability in a collection of 3D
models. This article surveys and classifies recent developments in this field,
outlines fundamental issues, discusses their potential applications in computer
vision and graphics, and highlights opportunities for future research. Our
primary goal is to bridge the gap between various techniques that have been
often independently proposed by different communities including mathematics and
statistics, computer vision and graphics, and medical image analysis.
The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs
Authors: S. V. Belim, S. B. Larionov
Date: 2018-12-25 00:00:00
Tasks: Community Detection

This article suggests an algorithm of impulse noise filtration, based on the
community detection in graphs. The image is representing as non-oriented
weighted graph. Each pixel of an image is corresponding to a vertex of the
graph. Community detection algorithm is running on the given graph. Assumed
that communities that contain only one pixel are corresponding to noised pixels
of an image. Suggested method was tested with help of computer experiment. This
experiment was conducted on grayscale, and on colored images, on artificial
images and on photos. It is shown that the suggested method is better than
median filter by 20% regardless of noise percent. Higher efficiency is
justified by the fact that most of filters are changing all of image pixels,
but suggested method is finding and restoring only noised pixels. The
dependence of the effectiveness of the proposed method on the percentage of
noise in the image is shown.
Trip Prediction by Leveraging Trip Histories from Neighboring Users
Authors: Yuxin Chen, Morteza Haghir Chehreghani
Date: 2018-12-25 00:00:00
Tasks: Prediction

We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy.
Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement
Authors: Suman Samui, Indrajit Chakrabarti, Soumya K. Ghosh
Date: 2018-12-25 00:00:00
Tasks: Speech Enhancement, Speech Separation

In recent years, Long Short-Term Memory (LSTM) has become a popular choice
for speech separation and speech enhancement task. The capability of LSTM
network can be enhanced by widening and adding more layers. However, this would
introduce millions of parameters in the network and also increase the
requirement of computational resources. These limitations hinders the efficient
implementation of RNN models in low-end devices such as mobile phones and
embedded systems with limited memory. To overcome these issues, we proposed to
use an efficient alternative approach of reducing parameters by representing
the weight matrix parameters of LSTM based on Tensor-Train (TT) format. We
called this Tensor-Train factorized LSTM as TT-LSTM model. Based on this
TT-LSTM units, we proposed a deep TensorNet model for single-channel speech
enhancement task. Experimental results in various test conditions and in terms
of standard speech quality and intelligibility metrics, demonstrated that the
proposed deep TT-LSTM based speech enhancement framework can achieve
competitive performances with the state-of-the-art uncompressed RNN model, even
though the proposed model architecture is orders of magnitude less complex.
Classification of X-Ray Protein Crystallization Using Deep Convolutional Neural Networks with a Finder Module
Authors: Yusei Miura, Tetsuya Sakurai, Claus Aranha, Toshiya Senda, Ryuichi Kato, Yusuke Yamada
Date: 2018-12-25 00:00:00
Tasks: General Classification

Recently, deep convolutional neural networks have shown good results for
image recognition. In this paper, we use convolutional neural networks with a
finder module, which discovers the important region for recognition and
extracts that region. We propose applying our method to the recognition of
protein crystals for X-ray structural analysis. In this analysis, it is
necessary to recognize states of protein crystallization from a large number of
images. There are several methods that realize protein crystallization
recognition by using convolutional neural networks. In each method, large-scale
data sets are required to recognize with high accuracy. In our data set, the
number of images is not good enough for training CNN. The amount of data for
CNN is a serious issue in various fields. Our method realizes high accuracy
recognition with few images by discovering the region where the crystallization
drop exists. We compared our crystallization image recognition method with a
high precision method using Inception-V3. We demonstrate that our method is
effective for crystallization images using several experiments. Our method
gained the AUC value that is about 5% higher than the compared method.
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome
Authors: Li Chen, Qi Li, Weiye Chen, Zeyu Wang, Haifeng Li
Date: 2018-12-25 00:00:00
Tasks: General Classification, Multi-Label Classification

Adversarial examples pose many security threats to convolutional neural networks (CNNs). Most defense algorithms prevent these threats by finding differences between the original images and adversarial examples. However, the found differences do not contain features about the classes, so these defense algorithms can only detect adversarial examples without recovering the correct labels. In this regard, we propose the Adversarial Feature Genome (AFG), a novel type of data that contains both the differences and features about classes. This method is inspired by an observed phenomenon, namely the Adversarial Feature Separability (AFS), where the difference between the feature maps of the original images and adversarial examples becomes larger with deeper layers. On top of that, we further develop an adversarial example recognition framework that detects adversarial examples and can recover the correct labels. In the experiments, the detection and classification of adversarial examples by AFGs has an accuracy of more than 90.01\% in various attack scenarios. To the best of our knowledge, our method is the first method that focuses on both attack detecting and recovering. AFG gives a new data-driven perspective to improve the robustness of CNNs. The source code is available at https://github.com/GeoX-Lab/Adv_Fea_Genome.