pwc-archive/papers-with-abstracts huggingface dataset:
Customer Lifetime Value Prediction Using Embeddings
Authors: Benjamin Paul Chamberlain, Angelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth
Date: 2017-03-07 00:00:00
Tasks: Marketing, Prediction, Value prediction

We describe the Customer LifeTime Value (CLTV) prediction system deployed at
ASOS.com, a global online fashion retailer. CLTV prediction is an important
problem in e-commerce where an accurate estimate of future value allows
retailers to effectively allocate marketing spend, identify and nurture high
value customers and mitigate exposure to losses. The system at ASOS provides
daily estimates of the future value of every customer and is one of the
cornerstones of the personalised shopping experience. The state of the art in
this domain uses large numbers of handcrafted features and ensemble regressors
to forecast value, predict churn and evaluate customer loyalty. Recently,
domains including language, vision and speech have shown dramatic advances by
replacing handcrafted features with features that are learned automatically
from data. We detail the system deployed at ASOS and show that learning feature
representations is a promising extension to the state of the art in CLTV
modelling. We propose a novel way to generate embeddings of customers, which
addresses the issue of the ever changing product catalogue and obtain a
significant improvement over an exhaustive set of handcrafted features.
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Authors: Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, Andrew Y. Ng
Date: 2017-07-06 00:00:00
Tasks: Arrhythmia Detection, Electrocardiography (ECG), Rhythm, Sensitivity

We develop an algorithm which exceeds the performance of board certified
cardiologists in detecting a wide range of heart arrhythmias from
electrocardiograms recorded with a single-lead wearable monitor. We build a
dataset with more than 500 times the number of unique patients than previously
studied corpora. On this dataset, we train a 34-layer convolutional neural
network which maps a sequence of ECG samples to a sequence of rhythm classes.
Committees of board-certified cardiologists annotate a gold standard test set
on which we compare the performance of our model to that of 6 other individual
cardiologists. We exceed the average cardiologist performance in both recall
(sensitivity) and precision (positive predictive value).
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference
Authors: Daniel Hsu
Date: 2017-07-03 00:00:00
Tasks: Time Series, Time Series Analysis, Variational Inference

In this paper, we use augmented the hierarchical latent variable model to
model multi-period time series, where the dynamics of time series are governed
by factors or trends in multiple periods. Previous methods based on stacked
recurrent neural network (RNN) and deep belief network (DBN) models cannot
model the tendencies in multiple periods, and no models for sequential data pay
special attention to redundant input variables which have no or even negative
impact on prediction and modeling. Applying hierarchical latent variable model
with multiple transition periods, our proposed algorithm can capture
dependencies in different temporal resolutions. Introducing Bayesian neural
network with Horseshoe prior as input network, we can discard the redundant
input variables in the optimization process, concurrently with the learning of
other parts of the model. Based on experiments with both synthetic and
real-world data, we show that the proposed method significantly improves the
modeling and prediction performance on multi-period time series.
Dr.VAE: Drug Response Variational Autoencoder
Authors: Ladislav Rampasek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg
Date: 2017-06-26 00:00:00
Tasks: Drug Response Prediction, General Classification

We present two deep generative models based on Variational Autoencoders to
improve the accuracy of drug response prediction. Our models, Perturbation
Variational Autoencoder and its semi-supervised extension, Drug Response
Variational Autoencoder (Dr.VAE), learn latent representation of the underlying
gene states before and after drug application that depend on: (i) drug-induced
biological change of each gene and (ii) overall treatment response outcome. Our
VAE-based models outperform the current published benchmarks in the field by
anywhere from 3 to 11% AUROC and 2 to 30% AUPR. In addition, we found that
better reconstruction accuracy does not necessarily lead to improvement in
classification accuracy and that jointly trained models perform better than
models that minimize reconstruction error independently.
Indefinite Kernel Logistic Regression with Concave-inexact-convex Procedure
Authors: Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens
Date: 2017-07-06 00:00:00
Tasks: regression

In kernel methods, the kernels are often required to be positive definite, which restricts the use of many indefinite kernels. To consider those non-positive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel logistic regression. The proposed indefinite kernel logistic regression (IKLR) model is analysed in the Reproducing Kernel Kre\u{\i}n Spaces (RKKS) and then becomes non-convex. Using the positive decomposition of a non-positive definite kernel, the derived IKLR model can be decomposed into the difference of two convex functions. Accordingly, a concave-convex procedure is introduced to solve the non-convex optimization problem. Since the concave-convex procedure has to solve a sub-problem in each iteration, we propose a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme to accelerate the solving process. Besides, we propose a stochastic variant of CCICP to efficiently obtain a proximal solution, which achieves the similar purpose with the inexact solving scheme in CCICP. The convergence analyses of the above two variants of concave-convex procedure are conducted. By doing so, our method works effectively not only under a deterministic setting but also under a stochastic setting. Experimental results on several benchmarks suggest that the proposed IKLR model performs favorably against the standard (positive-definite) kernel logistic regression and other competitive indefinite learning based algorithms.
Climbing the Kaggle Leaderboard by Exploiting the Log-Loss Oracle
Authors: Jacob Whitehill
Date: 2017-07-06 00:00:00
Tasks: Task 2

In the context of data-mining competitions (e.g., Kaggle, KDDCup, ILSVRC
Challenge), we show how access to an oracle that reports a contestant's
log-loss score on the test set can be exploited to deduce the ground-truth of
some of the test examples. By applying this technique iteratively to batches of
$m$ examples (for small $m$), all of the test labels can eventually be
inferred. In this paper, (1) We demonstrate this attack on the first stage of a
recent Kaggle competition (Intel & MobileODT Cancer Screening) and use it to
achieve a log-loss of $0.00000$ (and thus attain a rank of #4 out of 848
contestants), without ever training a classifier to solve the actual task. (2)
We prove an upper bound on the batch size $m$ as a function of the
floating-point resolution of the probability estimates that the contestant
submits for the labels. (3) We derive, and demonstrate in simulation, a more
flexible attack that can be used even when the oracle reports the accuracy on
an unknown (but fixed) subset of the test set's labels. These results underline
the importance of evaluating contestants based only on test data that the
oracle does not examine.
Nonparametric Marginal Analysis of Recurrent Events Data under Competing Risks
Authors: Bowen Li
Date: 2017-07-06 00:00:00
Tasks: Selection bias

This project was motivated by a dialysis study in northern Taiwan. Dialysis
patients, after shunt implantation, may experience two types ("acute" or
"non-acute") of shunt thrombosis, both of which may recur. We formulate the
problem under the framework of recurrent events data in the presence of
competing risks. In particular we focus on marginal inference for the gap time
variable of specific type. The functions of interest are the cumulative
incidence function and cause-specific hazard function. The major challenge of
nonparametric inference is the problem of induced dependent censoring. We apply
the technique of inverse probability of censoring weighting (IPCW) to adjust
for the selection bias. Besides point estimation, we apply the bootstrap
re-sampling method for further inference. Large sample properties of the
proposed estimators are derived. Simulations are performed to examine the
finite-sample performances of the proposed methods. Finally we apply the
proposed methodology to analyze the dialysis data.
Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases
Authors: Varun Kumar Ojha, Paramartha Dutta, Atal Chaudhuri, Hiranmay Saha
Date: 2017-07-06 00:00:00
Tasks: Sensitivity

Human fatalities are reported due to the excessive proportional presence of
hazardous gas components in the manhole, such as Hydrogen Sulfide, Ammonia,
Methane, Carbon Dioxide, Nitrogen Oxide, Carbon Monoxide, etc. Hence,
predetermination of these gases is imperative. A neural network (NN) based
intelligent sensory system is proposed for the avoidance of such fatalities.
Backpropagation (BP) was applied for the supervised training of the neural
network. A Gas sensor array consists of many sensor elements was employed for
the sensing manhole gases. Sensors in the sensor array are responsible for
sensing their target gas components only. Therefore, the presence of multiple
gases results in cross sensitivity. The cross sensitivity is a crucial issue to
this problem and it is viewed as pattern recognition and noise reduction
problem. Various performance parameters and complexity of the problem
influences NN training. In present chapter the performance of BP algorithm on
such a real life application problem was comprehensively studied, compared and
contrasted with the several other hybrid intelligent approaches both, in
theoretical and in the statistical sense.
ACO for Continuous Function Optimization: A Performance Analysis
Authors: Varun Kumar Ojha, Ajith Abraham, Vaclav Snasel
Date: 2017-07-06 00:00:00
Tasks: Diversity

The performance of the meta-heuristic algorithms often depends on their
parameter settings. Appropriate tuning of the underlying parameters can
drastically improve the performance of a meta-heuristic. The Ant Colony
Optimization (ACO), a population based meta-heuristic algorithm inspired by the
foraging behavior of the ants, is no different. Fundamentally, the ACO depends
on the construction of new solutions, variable by variable basis using Gaussian
sampling of the selected variables from an archive of solutions. A
comprehensive performance analysis of the underlying parameters such as:
selection strategy, distance measure metric and pheromone evaporation rate of
the ACO suggests that the Roulette Wheel Selection strategy enhances the
performance of the ACO due to its ability to provide non-uniformity and
adequate diversity in the selection of a solution. On the other hand, the
Squared Euclidean distance-measure metric offers better performance than other
distance-measure metrics. It is observed from the analysis that the ACO is
sensitive towards the evaporation rate. Experimental analysis between classical
ACO and other meta-heuristic suggested that the performance of the well-tuned
ACO surpasses its counterparts.
Simultaneous Optimization of Neural Network Weights and Active Nodes using Metaheuristics
Authors: Varun Kumar Ojha, Ajith Abraham, Vaclav Snasel
Date: 2017-07-06 00:00:00

Optimization of neural network (NN) significantly influenced by the transfer
function used in its active nodes. It has been observed that the homogeneity in
the activation nodes does not provide the best solution. Therefore, the
customizable transfer functions whose underlying parameters are subjected to
optimization were used to provide heterogeneity to NN. For the experimental
purpose, a meta-heuristic framework using a combined genotype representation of
connection weights and transfer function parameter was used. The performance of
adaptive Logistic, Tangent-hyperbolic, Gaussian and Beta functions were
analyzed. In present research work, concise comparisons between different
transfer function and between the NN optimization algorithms are presented. The
comprehensive analysis of the results obtained over the benchmark dataset
suggests that the Artificial Bee Colony with adaptive transfer function
provides the best results in terms of classification accuracy over the particle
swarm optimization and differential evolution.
Tensor-Train Recurrent Neural Networks for Video Classification
Authors: Yinchong Yang, Denis Krompass, Volker Tresp
Date: 2017-07-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Classification, General Classification, Video Classification

The Recurrent Neural Networks and their variants have shown promising
performances in sequence modeling tasks such as Natural Language Processing.
These models, however, turn out to be impractical and difficult to train when
exposed to very high-dimensional inputs due to the large input-to-hidden weight
matrix. This may have prevented RNNs' large-scale application in tasks that
involve very high input dimensions such as video modeling; current approaches
reduce the input dimensions using various feature extractors. To address this
challenge, we propose a new, more general and efficient approach by factorizing
the input-to-hidden weight matrix using Tensor-Train decomposition which is
trained simultaneously with the weights themselves. We test our model on
classification tasks using multiple real-world video datasets and achieve
competitive performances with state-of-the-art models, even though our model
architecture is orders of magnitude less complex. We believe that the proposed
approach provides a novel and fundamental building block for modeling
high-dimensional sequential data with RNN architectures and opens up many
possibilities to transfer the expressive and advanced architectures from other
domains such as NLP to modeling high-dimensional sequential data.
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Authors: Daniel Korenblum
Date: 2015-02-03 00:00:00
Tasks: Clustering, Community Detection, Dimensionality Reduction

Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.
The E2E Dataset: New Challenges For End-to-End Generation
Authors: Jekaterina Novikova, Ondřej Dušek, Verena Rieser
Date: 2017-06-28 00:00:00
Proceeding: WS 2017 8
Tasks: Data-to-Text Generation, Text Generation

This paper describes the E2E data, a new dataset for training end-to-end,
data-driven natural language generation systems in the restaurant domain, which
is ten times bigger than existing, frequently used datasets in this area. The
E2E dataset poses new challenges: (1) its human reference texts show more
lexical richness and syntactic variation, including discourse phenomena; (2)
generating from this set requires content selection. As such, learning from
this dataset promises more natural, varied and less template-like system
utterances. We also establish a baseline on this dataset, which illustrates
some of the difficulties associated with this data.
Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors
Authors: Chang Tang, Pichao Wang, Wanqing Li
Date: 2015-11-10 00:00:00
Tasks: Action Recognition, Incremental Learning, Temporal Action Localization

Different from traditional action recognition based on video segments, online
action recognition aims to recognize actions from unsegmented streams of data
in a continuous manner. One way for online recognition is based on the evidence
accumulation over time to make predictions from stream videos. This paper
presents a fast yet effective method to recognize actions from stream of noisy
skeleton data, and a novel weighted covariance descriptor is adopted to
accumulate evidence. In particular, a fast incremental updating method for the
weighted covariance descriptor is developed for accumulation of temporal
information and online prediction. The weighted covariance descriptor takes the
following principles into consideration: past frames have less contribution for
recognition and recent and informative frames such as key frames contribute
more to the recognition. The online recognition is achieved using a simple
nearest neighbor search against a set of offline trained action models.
Experimental results on MSC-12 Kinect Gesture dataset and our newly constructed
online action recognition dataset have demonstrated the efficacy of the
proposed method.
Skeleton-based Action Recognition Using LSTM and CNN
Authors: Chuankun Li, Pichao Wang, Shuang Wang, Yonghong Hou, Wanqing Li
Date: 2017-07-06 00:00:00
Tasks: Action Analysis, Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Recent methods based on 3D skeleton data have achieved outstanding
performance due to its conciseness, robustness, and view-independent
representation. With the development of deep learning, Convolutional Neural
Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have
achieved promising performance for action recognition. However, for CNN-based
methods, it is inevitable to loss temporal information when a sequence is
encoded into images. In order to capture as much spatial-temporal information
as possible, LSTM and CNN are adopted to conduct effective recognition with
later score fusion. In addition, experimental results show that the score
fusion between CNN and LSTM performs better than that between LSTM and LSTM for
the same feature. Our method achieved state-of-the-art results on NTU RGB+D
datasets for 3D human action analysis. The proposed method achieved 87.40% in
terms of accuracy and ranked $1^{st}$ place in Large Scale 3D Human Activity
Analysis Challenge in Depth Videos.
Application of Fuzzy Assessing for Reliability Decision Making
Authors: Shoele Jamali, Mehrdad J. Bani
Date: 2017-07-06 00:00:00
Tasks: Decision Making, Management

This paper proposes a new fuzzy assessing procedure with application in
management decision making. The proposed fuzzy approach build the membership
functions for system characteristics of a standby repairable system. This
method is used to extract a family of conventional crisp intervals from the
fuzzy repairable system for the desired system characteristics. This can be
determined with a set of nonlinear parametric programing using the membership
functions. When system characteristics are governed by the membership
functions, more information is provided for use by management, and because the
redundant system is extended to the fuzzy environment, general repairable
systems are represented more accurately and the analytic results are more
useful for designers and practitioners. Also beside standby, active redundancy
systems are used in many cases so this article has many practical instances.
Different from other studies, our model provides, a good estimated value based
on uncertain environments, a comparison discussion of using fuzzy theory and
conventional method and also a comparison between parallel (active redundancy)
and series system in fuzzy world when we have standby redundancy. When the
membership function intervals cannot be inverted explicitly, system management
or designers can specify the system characteristics of interest, perform
numerical calculations, examine the corresponding {\alpha}-cuts, and use this
information to develop or improve system processes.
Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection
Authors: Peter Makarov, Tatiana Ruzsics, Simon Clematide
Date: 2017-07-05 00:00:00
Proceeding: CONLL 2017 8
Tasks: Decoder, LEMMA

This paper presents the submissions by the University of Zurich to the
SIGMORPHON 2017 shared task on morphological reinflection. The task is to
predict the inflected form given a lemma and a set of morpho-syntactic
features. We focus on neural network approaches that can tackle the task in a
limited-resource setting. As the transduction of the lemma into the inflected
form is dominated by copying over lemma characters, we propose two recurrent
neural network architectures with hard monotonic attention that are strong at
copying and, yet, substantially different in how they achieve this. The first
approach is an encoder-decoder model with a copy mechanism. The second approach
is a neural state-transition system over a set of explicit edit actions,
including a designated COPY action. We experiment with character alignment and
find that naive, greedy alignment consistently produces strong results for some
languages. Our best system combination is the overall winner of the SIGMORPHON
2017 Shared Task 1 without external resources. At a setting with 100 training
samples, both our approaches, as ensembles of models, outperform the next best
competitor.
CNN features are also great at unsupervised classification
Authors: Joris Guérin, Olivier Gibaru, Stéphane Thiery, Eric Nyiri
Date: 2017-07-06 00:00:00
Tasks: Classification, Clustering, General Classification, Image Clustering

This paper aims at providing insight on the transferability of deep CNN
features to unsupervised problems. We study the impact of different pretrained
CNN feature extractors on the problem of image set clustering for object
classification as well as fine-grained classification. We propose a rather
straightforward pipeline combining deep-feature extraction using a CNN
pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in
image-clustering and provides better results. These results strengthen the
belief that supervised training of deep CNN on large datasets, with a large
variability of classes, extracts better features than most carefully designed
engineering approaches, even for unsupervised tasks. We also validate our
approach on a robotic application, consisting in sorting and storing objects
smartly based on clustering.