pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective
Authors: SamanehSorournejad, Zahra Zojaji, Reza Ebrahimi Atani, Amir Hassan Monadjemi
Date: 2016-11-19 00:00:00
Tasks: Anomaly Detection, Fraud Detection, General Classification

Credit card plays a very important rule in today's economy. It becomes an
unavoidable part of household, business and global activities. Although using
credit cards provides enormous benefits when used carefully and
responsibly,significant credit and financial damages may be caused by
fraudulent activities. Many techniques have been proposed to confront the
growth in credit card fraud. However, all of these techniques have the same
goal of avoiding the credit card fraud; each one has its own drawbacks,
advantages and characteristics. In this paper, after investigating difficulties
of credit card fraud detection, we seek to review the state of the art in
credit card fraud detection techniques, data sets and evaluation criteria.The
advantages and disadvantages of fraud detection methods are enumerated and
compared.Furthermore, a classification of mentioned techniques into two main
fraud detection approaches, namely, misuses (supervised) and anomaly detection
(unsupervised) is presented. Again, a classification of techniques is proposed
based on capability to process the numerical and categorical data sets.
Different data sets used in literature are then described and grouped into real
and synthesized data and the effective and common attributes are extracted for
further usage.Moreover, evaluation employed criterions in literature are
collected and discussed.Consequently, open issues for credit card fraud
detection are explained as guidelines for new researchers.
Gated Neural Networks for Option Pricing: Rationality by Design
Authors: Yongxin Yang, Yu Zheng, Timothy M. Hospedales
Date: 2016-09-14 00:00:00
Tasks: Inductive Bias, valid

We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model's predictions, and provides econometrically useful byproduct such as risk neutral density.
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Authors: Emily Denton, Sam Gross, Rob Fergus
Date: 2016-11-19 00:00:00
Tasks: Image Classification, Semi-Supervised Image Classification

We introduce a simple semi-supervised learning approach for images based on
in-painting using an adversarial loss. Images with random patches removed are
presented to a generator whose task is to fill in the hole, based on the
surrounding pixels. The in-painted images are then presented to a discriminator
network that judges if they are real (unaltered training images) or not. This
task acts as a regularizer for standard supervised training of the
discriminator. Using our approach we are able to directly train large VGG-style
networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL
datasets, where our approach obtains performance comparable or superior to
existing methods.
Semantic tracking: Single-target tracking with inter-supervised convolutional networks
Authors: Jingjing Xiao, Qiang Lan, Linbo Qiao, Ales Leonardis
Date: 2016-11-19 00:00:00
Tasks: General Classification, Object

This article presents a semantic tracker which simultaneously tracks a single
target and recognises its category. In general, it is hard to design a tracking
model suitable for all object categories, e.g., a rigid tracker for a car is
not suitable for a deformable gymnast. Category-based trackers usually achieve
superior tracking performance for the objects of that specific category, but
have difficulties being generalised. Therefore, we propose a novel unified
robust tracking framework which explicitly encodes both generic features and
category-based features. The tracker consists of a shared convolutional network
(NetS), which feeds into two parallel networks, NetC for classification and
NetT for tracking. NetS is pre-trained on ImageNet to serve as a generic
feature extractor across the different object categories for NetC and NetT.
NetC utilises those features within fully connected layers to classify the
object category. NetT has multiple branches, corresponding to multiple
categories, to distinguish the tracked object from the background. Since each
branch in NetT is trained by the videos of a specific category or groups of
similar categories, NetT encodes category-based features for tracking. During
online tracking, NetC and NetT jointly determine the target regions with the
right category and foreground labels for target estimation. To improve the
robustness and precision, NetC and NetT inter-supervise each other and trigger
network adaptation when their outputs are ambiguous for the same image regions
(i.e., when the category label contradicts the foreground/background
classification). We have compared the performance of our tracker to other
state-of-the-art trackers on a large-scale tracking benchmark (100
sequences)---the obtained results demonstrate the effectiveness of our proposed
tracker as it outperformed other 38 state-of-the-art tracking algorithms.
Ordinal Constrained Binary Code Learning for Nearest Neighbor Search
Authors: Hong Liu, Rongrong Ji, Yongjian Wu, Feiyue Huang
Date: 2016-11-19 00:00:00
Tasks: Retrieval, Small Data Image Classification

Recent years have witnessed extensive attention in binary code learning,
a.k.a. hashing, for nearest neighbor search problems. It has been seen that
high-dimensional data points can be quantized into binary codes to give an
efficient similarity approximation via Hamming distance. Among existing
schemes, ranking-based hashing is recent promising that targets at preserving
ordinal relations of ranking in the Hamming space to minimize retrieval loss.
However, the size of the ranking tuples, which shows the ordinal relations, is
quadratic or cubic to the size of training samples. By given a large-scale
training data set, it is very expensive to embed such ranking tuples in binary
code learning. Besides, it remains a dificulty to build ranking tuples
efficiently for most ranking-preserving hashing, which are deployed over an
ordinal graph-based setting. To handle these problems, we propose a novel
ranking-preserving hashing method, dubbed Ordinal Constraint Hashing (OCH),
which efficiently learns the optimal hashing functions with a graph-based
approximation to embed the ordinal relations. The core idea is to reduce the
size of ordinal graph with ordinal constraint projection, which preserves the
ordinal relations through a small data set (such as clusters or random
samples). In particular, to learn such hash functions effectively, we further
relax the discrete constraints and design a specific stochastic gradient decent
algorithm for optimization. Experimental results on three large-scale visual
search benchmark datasets, i.e. LabelMe, Tiny100K and GIST1M, show that the
proposed OCH method can achieve superior performance over the state-of-the-arts
approaches.
Invertible Conditional GANs for image editing
Authors: Guim Perarnau, Joost Van de Weijer, Bogdan Raducanu, Jose M. Álvarez
Date: 2016-11-19 00:00:00
Tasks: Conditional Image Generation, Image-to-Image Translation

Generative Adversarial Networks (GANs) have recently demonstrated to
successfully approximate complex data distributions. A relevant extension of
this model is conditional GANs (cGANs), where the introduction of external
information allows to determine specific representations of the generated
images. In this work, we evaluate encoders to inverse the mapping of a cGAN,
i.e., mapping a real image into a latent space and a conditional
representation. This allows, for example, to reconstruct and modify real images
of faces conditioning on arbitrary attributes. Additionally, we evaluate the
design of cGANs. The combination of an encoder with a cGAN, which we call
Invertible cGAN (IcGAN), enables to re-generate real images with deterministic
complex modifications.
Quantized neural network design under weight capacity constraint
Authors: Sungho Shin, Kyuyeon Hwang, Wonyong Sung
Date: 2016-11-19 00:00:00
Tasks: Quantization

The complexity of deep neural network algorithms for hardware implementation
can be lowered either by scaling the number of units or reducing the
word-length of weights. Both approaches, however, can accompany the performance
degradation although many types of research are conducted to relieve this
problem. Thus, it is an important question which one, between the network size
scaling and the weight quantization, is more effective for hardware
optimization. For this study, the performances of fully-connected deep neural
networks (FCDNNs) and convolutional neural networks (CNNs) are evaluated while
changing the network complexity and the word-length of weights. Based on these
experiments, we present the effective compression ratio (ECR) to guide the
trade-off between the network size and the precision of weights when the
hardware resource is limited.
Feature Selection Library (MATLAB Toolbox)
Authors: Giorgio Roffo
Date: 2016-07-05 00:00:00
Tasks: feature selection, General Classification

The Feature Selection Library (FSLib) signifies a notable progression in machine learning and data mining for MATLAB users, emphasizing the critical role of Feature Selection (FS) in enhancing model efficiency and effectiveness by pinpointing essential features for specific tasks. FSLib's contributions are comprehensive, tackling various FS challenges. It offers a wide array of FS algorithms, including filter, embedded, and wrapper methods, allowing for optimal feature selection tailored to specific problem requirements. Filter methods prioritize intrinsic feature properties, embedded methods integrate selection within the training process, and wrapper methods evaluate features based on model performance, catering to diverse modeling approaches. FSLib also addresses the curse of dimensionality by facilitating the selection of relevant feature subsets, thereby reducing data dimensionality, lessening computational demands, and potentially improving model generalizability. Furthermore, by eliminating superfluous features, FSLib streamlines the learning process, enhancing model training efficiency and scalability. This targeted selection process not only accelerates model development but also bolsters model accuracy, precision, and recall by concentrating on crucial information. Additionally, FSLib enhances data interpretability, offering insights into data structure through the identification of significant features, thereby aiding in pattern discovery and understanding. In essence, FSLib extends beyond simple feature selection, providing a comprehensive framework that augments the entire machine learning and data mining workflow. By presenting an extensive selection of algorithms, mitigating dimensional challenges, expediting learning, improving model metrics, and fostering data insight, FSLib emerges as an instrumental resource in the evolution of machine learning research and practice.
Source Localization on Graphs via l1 Recovery and Spectral Graph Theory
Authors: Rodrigo Pena, Xavier Bresson, Pierre Vandergheynst
Date: 2016-03-24 00:00:00

We cast the problem of source localization on graphs as the simultaneous
problem of sparse recovery and diffusion kernel learning. An l1 regularization
term enforces the sparsity constraint while we recover the sources of diffusion
from a single snapshot of the diffusion process. The diffusion kernel is
estimated by assuming the process to be as generic as the standard heat
diffusion. We show with synthetic data that we can concomitantly learn the
diffusion kernel and the sources, given an estimated initialization. We
validate our model with cholera mortality and atmospheric tracer diffusion
data, showing also that the accuracy of the solution depends on the
construction of the graph from the data points.
Spotting Rumors via Novelty Detection
Authors: Yumeng Qin, Dominik Wurzer, Victor Lavrenko, Cunchen Tang
Date: 2016-11-19 00:00:00
Tasks: Articles, Novelty Detection, Rumour Detection

Rumour detection is hard because the most accurate systems operate
retrospectively, only recognizing rumours once they have collected repeated
signals. By then the rumours might have already spread and caused harm. We
introduce a new category of features based on novelty, tailored to detect
rumours early on. To compensate for the absence of repeated signals, we make
use of news wire as an additional data source. Unconfirmed (novel) information
with respect to the news articles is considered as an indication of rumours.
Additionally we introduce pseudo feedback, which assumes that documents that
are similar to previous rumours, are more likely to also be a rumour.
Comparison with other real-time approaches shows that novelty based features in
conjunction with pseudo feedback perform significantly better, when detecting
rumours instantly after their publication.
Determining the Veracity of Rumours on Twitter
Authors: Georgios Giasemidis, Colin Singleton, Ioannis Agrafiotis, Jason R. C. Nurse, Alan Pilgrim, Chris Willis, Danica Vukadinovic Greetham
Date: 2016-11-19 00:00:00
Tasks: Misinformation, Rumour Detection

While social networks can provide an ideal platform for up-to-date
information from individuals across the world, it has also proved to be a place
where rumours fester and accidental or deliberate misinformation often emerges.
In this article, we aim to support the task of making sense from social media
data, and specifically, seek to build an autonomous message-classifier that
filters relevant and trustworthy information from Twitter. For our work, we
collected about 100 million public tweets, including users' past tweets, from
which we identified 72 rumours (41 true, 31 false). We considered over 80
trustworthiness measures including the authors' profile and past behaviour, the
social network connections (graphs), and the content of tweets themselves. We
ran modern machine-learning classifiers over those measures to produce
trustworthiness scores at various time windows from the outbreak of the rumour.
Such time-windows were key as they allowed useful insight into the progression
of the rumours. From our findings, we identified that our model was
significantly more accurate than similar studies in the literature. We also
identified critical attributes of the data that give rise to the
trustworthiness scores assigned. Finally we developed a software demonstration
that provides a visual user interface to allow the user to examine the
analysis.
Cross-model convolutional neural network for multiple modality data representation
Authors: Yanbin Wu, Li Wang, Fan Cui, Hongbin Zhai, Baoming Dong, Jim Jing-Yan Wang
Date: 2016-11-19 00:00:00

A novel data representation method of convolutional neural net- work (CNN) is
proposed in this paper to represent data of different modalities. We learn a
CNN model for the data of each modality to map the data of differ- ent
modalities to a common space, and regularize the new representations in the
common space by a cross-model relevance matrix. We further impose that the
class label of data points can also be predicted from the CNN representa- tions
in the common space. The learning problem is modeled as a minimiza- tion
problem, which is solved by an augmented Lagrange method (ALM) with updating
rules of Alternating direction method of multipliers (ADMM). The experiments
over benchmark of sequence data of multiple modalities show its advantage.
Inferring Restaurant Styles by Mining Crowd Sourced Photos from User-Review Websites
Authors: Haofu Liao, Yuncheng Li, Tianran Hu, Jiebo Luo
Date: 2016-11-19 00:00:00
Tasks: Informativeness, Multi-Label Learning, TAG

When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaurant review websites to provide their users an intuitive and effective search experience. In this paper, we present a novel approach to inferring restaurant types or styles (ambiance, dish styles, suitability for different occasions) from user uploaded photos on user-review websites. To that end, we first collect a novel restaurant photo dataset associating the user contributed photos with the restaurant styles from TripAdvior. We then propose a deep multi-instance multi-label learning (MIML) framework to deal with the unique problem setting of the restaurant style classification task. We employ a two-step bootstrap strategy to train a multi-label convolutional neural network (CNN). The multi-label CNN is then used to compute the confidence scores of restaurant styles for all the images associated with a restaurant. The computed confidence scores are further used to train a final binary classifier for each restaurant style tag. Upon training, the styles of a restaurant can be profiled by analyzing restaurant photos with the trained multi-label CNN and SVM models. Experimental evaluation has demonstrated that our crowd sourcing-based approach can effectively infer the restaurant style when there are a sufficient number of user uploaded photos for a given restaurant.
A Bayesian approach to type-specific conic fitting
Authors: Matthew Collett
Date: 2016-11-19 00:00:00
Tasks: Vocal Bursts Type Prediction

A perturbative approach is used to quantify the effect of noise in data
points on fitted parameters in a general homogeneous linear model, and the
results applied to the case of conic sections. There is an optimal choice of
normalisation that minimises bias, and iteration with the correct reweighting
significantly improves statistical reliability. By conditioning on an
appropriate prior, an unbiased type-specific fit can be obtained. Error
estimates for the conic coefficients may also be used to obtain both bias
corrections and confidence intervals for other curve parameters.
Ternary Weight Networks
Authors: Fengfu Li, Bin Liu, Xiaoxing Wang, Bo Zhang, Junchi Yan
Date: 2016-05-16 00:00:00
Tasks: Model Compression, object-detection, Object Detection

We present a memory and computation efficient ternary weight networks (TWNs) - with weights constrained to +1, 0 and -1. The Euclidian distance between full (float or double) precision weights and the ternary weights along with a scaling factor is minimized in training stage. Besides, a threshold-based ternary function is optimized to get an approximated solution which can be fast and easily computed. TWNs have shown better expressive abilities than binary precision counterparts. Meanwhile, TWNs achieve up to 16$\times$ model compression rate and need fewer multiplications compared with the float32 precision counterparts. Extensive experiments on MNIST, CIFAR-10, and ImageNet datasets show that the TWNs achieve much better result than the Binary-Weight-Networks (BWNs) and the classification performance on MNIST and CIFAR-10 is very close to the full precision networks. We also verify our method on object detection task and show that TWNs significantly outperforms BWN by more than 10\% mAP on PASCAL VOC dataset. The pytorch version of source code is available at: https://github.com/Thinklab-SJTU/twns.
Binarized Neural Networks on the ImageNet Classification Task
Authors: Xundong Wu, Yong Wu, Yong Zhao
Date: 2016-04-11 00:00:00
Tasks: Classification, General Classification

We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet
ILSVRC-2102 dataset classification task and achieved a good performance. With a
moderate size network of 13 layers, we obtained top-5 classification accuracy
rate of 84.1 % on validation set through network distillation, much better than
previous published results of 73.2% on XNOR network and 69.1% on binarized
GoogleNET. We expect networks of better performance can be obtained by
following our current strategies. We provide a detailed discussion and
preliminary analysis on strategies used in the network training.
dMath: Distributed Linear Algebra for DL
Authors: Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale
Date: 2016-11-19 00:00:00
Tasks: GPU, Management, Math

The paper presents a parallel math library, dMath, that demonstrates leading
scaling when using intranode, internode, and hybrid-parallelism for deep
learning (DL). dMath provides easy-to-use distributed primitives and a variety
of domain-specific algorithms including matrix multiplication, convolutions,
and others allowing for rapid development of scalable applications like deep
neural networks (DNNs). Persistent data stored in GPU memory and advanced
memory management techniques avoid costly transfers between host and device.
dMath delivers performance, portability, and productivity to its specific
domain of support.
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
Authors: John Duchi, Peter Glynn, Hongseok Namkoong
Date: 2016-10-11 00:00:00
Tasks: Stochastic Optimization

We study statistical inference and distributionally robust solution methods
for stochastic optimization problems, focusing on confidence intervals for
optimal values and solutions that achieve exact coverage asymptotically. We
develop a generalized empirical likelihood framework---based on distributional
uncertainty sets constructed from nonparametric $f$-divergence balls---for
Hadamard differentiable functionals, and in particular, stochastic optimization
problems. As consequences of this theory, we provide a principled method for
choosing the size of distributional uncertainty regions to provide one- and
two-sided confidence intervals that achieve exact coverage. We also give an
asymptotic expansion for our distributionally robust formulation, showing how
robustification regularizes problems by their variance. Finally, we show that
optimizers of the distributionally robust formulations we study enjoy
(essentially) the same consistency properties as those in classical sample
average approximations. Our general approach applies to quickly mixing
stationary sequences, including geometrically ergodic Harris recurrent Markov
chains.
Word Sense Disambiguation using a Bidirectional LSTM
Authors: Mikael Kågebäck, Hans Salomonsson
Date: 2016-06-11 00:00:00
Proceeding: WS 2016 12
Tasks: Word Sense Disambiguation

In this paper we present a clean, yet effective, model for word sense
disambiguation. Our approach leverage a bidirectional long short-term memory
network which is shared between all words. This enables the model to share
statistical strength and to scale well with vocabulary size. The model is
trained end-to-end, directly from the raw text to sense labels, and makes
effective use of word order. We evaluate our approach on two standard datasets,
using identical hyperparameter settings, which are in turn tuned on a third set
of held out data. We employ no external resources (e.g. knowledge graphs,
part-of-speech tagging, etc), language specific features, or hand crafted
rules, but still achieve statistically equivalent results to the best
state-of-the-art systems, that employ no such limitations.