pwc-archive/papers-with-abstracts huggingface dataset:
TransNets: Learning to Transform for Recommendation
Authors: Rose Catherine, William Cohen
Date: 2017-04-07 00:00:00
Tasks: Recommendation Systems

Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.
Probabilistic Active Learning of Functions in Structural Causal Models
Authors: Paul K. Rubenstein, Ilya Tolstikhin, Philipp Hennig, Bernhard Schoelkopf
Date: 2017-06-30 00:00:00
Tasks: Active Learning, Causal Discovery

We consider the problem of learning the functions computing children from
parents in a Structural Causal Model once the underlying causal graph has been
identified. This is in some sense the second step after causal discovery.
Taking a probabilistic approach to estimating these functions, we derive a
natural myopic active learning scheme that identifies the intervention which is
optimally informative about all of the unknown functions jointly, given
previously observed data. We test the derived algorithms on simple examples, to
demonstrate that they produce a structured exploration policy that
significantly improves on unstructured base-lines.
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time
Authors: Alexander Dallmann, Alexander Grimm, Christian Pölitz, Daniel Zoller, Andreas Hotho
Date: 2017-06-30 00:00:00
Tasks: Model Selection, Session-Based Recommendations

Recently, Recurrent Neural Networks (RNNs) have been applied to the task of
session-based recommendation. These approaches use RNNs to predict the next
item in a user session based on the previ- ously visited items. While some
approaches consider additional item properties, we argue that item dwell time
can be used as an implicit measure of user interest to improve session-based
item recommen- dations. We propose an extension to existing RNN approaches that
captures user dwell time in addition to the visited items and show that
recommendation performance can be improved. Additionally, we investigate the
usefulness of a single validation split for model selection in the case of
minor improvements and find that in our case the best model is not selected and
a fold-like study with different validation sets is necessary to ensure the
selection of the best model.
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Authors: Ala Mhalla, Thierry Chateau, Houda Maamatou, Sami Gazzah, Najoua Essoukri Ben Amara
Date: 2017-06-30 00:00:00
Tasks: Transfer Learning

Generally, the performance of a generic detector decreases significantly when
it is tested on a specific scene due to the large variation between the source
training dataset and the samples from the target scene. To solve this problem,
we propose a new formalism of transfer learning based on the theory of a
Sequential Monte Carlo (SMC) filter to automatically specialize a
scene-specific Faster R-CNN detector. The suggested framework uses different
strategies based on the SMC filter steps to approximate iteratively the target
distribution as a set of samples in order to specialize the Faster R-CNN
detector towards a target scene. Moreover, we put forward a likelihood function
that combines spatio-temporal information extracted from the target video
sequence and the confidence-score given by the output layer of the Faster
R-CNN, to favor the selection of target samples associated with the right
label. The effectiveness of the suggested framework is demonstrated through
experiments on several public traffic datasets. Compared with the
state-of-the-art specialization frameworks, the proposed framework presents
encouraging results for both single and multi-traffic object detections.
On Fairness, Diversity and Randomness in Algorithmic Decision Making
Authors: Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi, Adrian Weller
Date: 2017-06-30 00:00:00
Tasks: Decision Making, Diversity, Fairness

Consider a binary decision making process where a single machine learning
classifier replaces a multitude of humans. We raise questions about the
resulting loss of diversity in the decision making process. We study the
potential benefits of using random classifier ensembles instead of a single
classifier in the context of fairness-aware learning and demonstrate various
attractive properties: (i) an ensemble of fair classifiers is guaranteed to be
fair, for several different measures of fairness, (ii) an ensemble of unfair
classifiers can still achieve fair outcomes, and (iii) an ensemble of
classifiers can achieve better accuracy-fairness trade-offs than a single
classifier. Finally, we introduce notions of distributional fairness to
characterize further potential benefits of random classifier ensembles.
Elementary epistemological features of machine intelligence
Authors: Marko Horvat
Date: 2008-12-04 00:00:00
Tasks: General Classification

Theoretical analysis of machine intelligence (MI) is useful for defining a
common platform in both theoretical and applied artificial intelligence (AI).
The goal of this paper is to set canonical definitions that can assist
pragmatic research in both strong and weak AI. Described epistemological
features of machine intelligence include relationship between intelligent
behavior, intelligent and unintelligent machine characteristics, observable and
unobservable entities and classification of intelligence. The paper also
establishes algebraic definitions of efficiency and accuracy of MI tests as
their quality measure. The last part of the paper addresses the learning
process with respect to the traditional epistemology and the epistemology of MI
described here. The proposed views on MI positively correlate to the Hegelian
monistic epistemology and contribute towards amalgamating idealistic
deliberations with the AI theory, particularly in a local frame of reference.
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning
Authors: Frank E. Curtis, Katya Scheinberg
Date: 2017-06-30 00:00:00
Tasks: BIG-bench Machine Learning, regression, Second-order methods

The goal of this tutorial is to introduce key models, algorithms, and open
questions related to the use of optimization methods for solving problems
arising in machine learning. It is written with an INFORMS audience in mind,
specifically those readers who are familiar with the basics of optimization
algorithms, but less familiar with machine learning. We begin by deriving a
formulation of a supervised learning problem and show how it leads to various
optimization problems, depending on the context and underlying assumptions. We
then discuss some of the distinctive features of these optimization problems,
focusing on the examples of logistic regression and the training of deep neural
networks. The latter half of the tutorial focuses on optimization algorithms,
first for convex logistic regression, for which we discuss the use of
first-order methods, the stochastic gradient method, variance reducing
stochastic methods, and second-order methods. Finally, we discuss how these
approaches can be employed to the training of deep neural networks, emphasizing
the difficulties that arise from the complex, nonconvex structure of these
models.
An Expectation-Maximization Algorithm for the Fractal Inverse Problem
Authors: Peter Bloem, Steven de Rooij
Date: 2017-06-09 00:00:00
Tasks: parameter estimation

We present an Expectation-Maximization algorithm for the fractal inverse
problem: the problem of fitting a fractal model to data. In our setting the
fractals are Iterated Function Systems (IFS), with similitudes as the family of
transformations. The data is a point cloud in ${\mathbb R}^H$ with arbitrary
dimension $H$. Each IFS defines a probability distribution on ${\mathbb R}^H$,
so that the fractal inverse problem can be cast as a problem of parameter
estimation. We show that the algorithm reconstructs well-known fractals from
data, with the model converging to high precision parameters. We also show the
utility of the model as an approximation for datasources outside the IFS model
class.
Rule-Mining based classification: a benchmark study
Authors: Margaux Luck, Nicolas Pallet, Cecilia Damon
Date: 2017-06-30 00:00:00
Tasks: Classification, General Classification, regression

This study proposed an exhaustive stable/reproducible rule-mining algorithm
combined to a classifier to generate both accurate and interpretable models.
Our method first extracts rules (i.e., a conjunction of conditions about the
values of a small number of input features) with our exhaustive rule-mining
algorithm, then constructs a new feature space based on the most relevant rules
called "local features" and finally, builds a local predictive model by
training a standard classifier on the new local feature space. This local
feature space is easy interpretable by providing a human-understandable
explanation under the explicit form of rules. Furthermore, our local predictive
approach is as powerful as global classical ones like logistic regression (LR),
support vector machine (SVM) and rules based methods like random forest (RF)
and gradient boosted tree (GBT).
A reliability-based approach for influence maximization using the evidence theory
Authors: Siwar Jendoubi, Arnaud Martin
Date: 2017-06-30 00:00:00
Tasks: Marketing

The influence maximization is the problem of finding a set of social network
users, called influencers, that can trigger a large cascade of propagation.
Influencers are very beneficial to make a marketing campaign goes viral through
social networks for example. In this paper, we propose an influence measure
that combines many influence indicators. Besides, we consider the reliability
of each influence indicator and we present a distance-based process that allows
to estimate the reliability of each indicator. The proposed measure is defined
under the framework of the theory of belief functions. Furthermore, the
reliability-based influence measure is used with an influence maximization
model to select a set of users that are able to maximize the influence in the
network. Finally, we present a set of experiments on a dataset collected from
Twitter. These experiments show the performance of the proposed solution in
detecting social influencers with good quality.
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
Authors: Yingying Jiang, Xiangyu Zhu, Xiaobing Wang, Shuli Yang, Wei Li, Hua Wang, Pei Fu, Zhenbo Luo
Date: 2017-06-29 00:00:00
Tasks: Region Proposal, Scene Text Detection, Text Detection

In this paper, we propose a novel method called Rotational Region CNN (R2CNN)
for detecting arbitrary-oriented texts in natural scene images. The framework
is based on Faster R-CNN [1] architecture. First, we use the Region Proposal
Network (RPN) to generate axis-aligned bounding boxes that enclose the texts
with different orientations. Second, for each axis-aligned text box proposed by
RPN, we extract its pooled features with different pooled sizes and the
concatenated features are used to simultaneously predict the text/non-text
score, axis-aligned box and inclined minimum area box. At last, we use an
inclined non-maximum suppression to get the detection results. Our approach
achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR
2013.
Statistical Analysis of Dice CAPTCHA Usability
Authors: Darko Brodić, Alessia Amelio, Ivo R. Draganov
Date: 2017-06-30 00:00:00

In this paper the elements of the CAPTCHA usability are analyzed. CAPTCHA, as
a time progressive element in computer science, has been under constant
interest of ordinary, professional as well as the scientific users of the
Internet. The analysis is given based on the usability elements of CAPTCHA
which are abbreviated as user-centric approach to the CAPTCHA. To demonstrate
it, the specific type of Dice CAPTCHA is used in the experiment. The experiment
is conducted on 190 Internet users with different demographic characteristics
on laptop and tablet computers. The obtained results are statistically
processed. At the end, the results are compared and conclusion of their use is
drawn.
Prepaid or Postpaid? That is the question. Novel Methods of Subscription Type Prediction in Mobile Phone Services
Authors: Yongjun Liao, Wei Du, Márton Karsai, Carlos Sarraute, Martin Minnoni, Eric Fleury
Date: 2017-06-30 00:00:00
Tasks: General Classification, Marketing, Type prediction

In this paper we investigate the behavioural differences between mobile phone
customers with prepaid and postpaid subscriptions. Our study reveals that (a)
postpaid customers are more active in terms of service usage and (b) there are
strong structural correlations in the mobile phone call network as connections
between customers of the same subscription type are much more frequent than
those between customers of different subscription types. Based on these
observations we provide methods to detect the subscription type of customers by
using information about their personal call statistics, and also their
egocentric networks simultaneously. The key of our first approach is to cast
this classification problem as a problem of graph labelling, which can be
solved by max-flow min-cut algorithms. Our experiments show that, by using both
user attributes and relationships, the proposed graph labelling approach is
able to achieve a classification accuracy of $\sim 87\%$, which outperforms by
$\sim 7\%$ supervised learning methods using only user attributes. In our
second problem we aim to infer the subscription type of customers of external
operators. We propose via approximate methods to solve this problem by using
node attributes, and a two-ways indirect inference method based on observed
homophilic structural correlations. Our results have straightforward
applications in behavioural prediction and personal marketing.
Bottleneck Conditional Density Estimation
Authors: Rui Shu, Hung H. Bui, Mohammad Ghavamzadeh
Date: 2016-11-25 00:00:00
Proceeding: ICML 2017 8
Tasks: Density Estimation

We introduce a new framework for training deep generative models for
high-dimensional conditional density estimation. The Bottleneck Conditional
Density Estimator (BCDE) is a variant of the conditional variational
autoencoder (CVAE) that employs layer(s) of stochastic variables as the
bottleneck between the input $x$ and target $y$, where both are
high-dimensional. Crucially, we propose a new hybrid training method that
blends the conditional generative model with a joint generative model. Hybrid
blending is the key to effective training of the BCDE, which avoids overfitting
and provides a novel mechanism for leveraging unlabeled data. We show that our
hybrid training procedure enables models to achieve competitive results in the
MNIST quadrant prediction task in the fully-supervised setting, and sets new
benchmarks in the semi-supervised regime for MNIST, SVHN, and CelebA.
A ROS multi-ontology references services: OWL reasoners and application prototyping issues
Authors: Luca Buoncompagni, Alessio Capitanelli, Fulvio Mastrogiovanni
Date: 2017-06-30 00:00:00

This paper introduces a ROS Multi Ontology References (ARMOR) service, a general-purpose and scalable interface between robot architectures and OWL reasoners. ARMOR addresses synchronisation and communication issues among heterogeneous and distributed software components. As a guiding scenario, we consider a prototyping approach for the use of symbolic reasoning in human-robot interaction applications.
Restricted Causal Inference Algorithm
Authors: Mieczysław A. Kłopotek
Date: 2017-06-30 00:00:00
Tasks: Causal Inference

This paper proposes a new algorithm for recovery of belief network structure
from data handling hidden variables. It consists essentially in an extension of
the CI algorithm of Spirtes et al. by restricting the number of conditional
dependencies checked up to k variables and in an extension of the original CI
by additional steps transforming so called partial including path graph into a
belief network. Its correctness is demonstrated.
Stable Architectures for Deep Neural Networks
Authors: Eldad Haber, Lars Ruthotto
Date: 2017-05-09 00:00:00
Tasks: Deep Learning, parameter estimation

Deep neural networks have become invaluable tools for supervised machine
learning, e.g., classification of text or images. While often offering superior
results over traditional techniques and successfully expressing complicated
patterns in data, deep architectures are known to be challenging to design and
train such that they generalize well to new data. Important issues with deep
architectures are numerical instabilities in derivative-based learning
algorithms commonly called exploding or vanishing gradients. In this paper we
propose new forward propagation techniques inspired by systems of Ordinary
Differential Equations (ODE) that overcome this challenge and lead to
well-posed learning problems for arbitrarily deep networks.
  The backbone of our approach is our interpretation of deep learning as a
parameter estimation problem of nonlinear dynamical systems. Given this
formulation, we analyze stability and well-posedness of deep learning and use
this new understanding to develop new network architectures. We relate the
exploding and vanishing gradient phenomenon to the stability of the discrete
ODE and present several strategies for stabilizing deep learning for very deep
networks. While our new architectures restrict the solution space, several
numerical experiments show their competitiveness with state-of-the-art
networks.
Barankin Vector Locally Best Unbiased Estimates
Authors: Bruno Cernuschi-Frias
Date: 2017-06-30 00:00:00

The Barankin bound is generalized to the vector case in the mean square error
sense. Necessary and sufficient conditions are obtained to achieve the lower
bound. To obtain the result, a simple finite dimensional real vector valued
generalization of the Riesz representation theorem for Hilbert spaces is given.
The bound has the form of a linear matrix inequality where the covariances of
any unbiased estimator, if these exist, are lower bounded by matrices depending
only on the parametrized probability distributions.
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
Authors: Nicolò Cesa-Bianchi, Pierre Gaillard, Claudio Gentile, Sébastien Gerchinovitz
Date: 2017-02-27 00:00:00

We investigate contextual online learning with nonparametric (Lipschitz)
comparison classes under different assumptions on losses and feedback
information. For full information feedback and Lipschitz losses, we design the
first explicit algorithm achieving the minimax regret rate (up to log factors).
In a partial feedback model motivated by second-price auctions, we obtain
algorithms for Lipschitz and semi-Lipschitz losses with regret bounds improving
on the known bounds for standard bandit feedback. Our analysis combines novel
results for contextual second-price auctions with a novel algorithmic approach
based on chaining. When the context space is Euclidean, our chaining approach
is efficient and delivers an even better regret bound.
Attentive Recurrent Comparators
Authors: Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8
Tasks: General Classification, One-Shot Learning

Rapid learning requires flexible representations to quickly adopt to new
evidence. We develop a novel class of models called Attentive Recurrent
Comparators (ARCs) that form representations of objects by cycling through them
and making observations. Using the representations extracted by ARCs, we
develop a way of approximating a \textit{dynamic representation space} and use
it for one-shot learning. In the task of one-shot classification on the
Omniglot dataset, we achieve the state of the art performance with an error
rate of 1.5\%. This represents the first super-human result achieved for this
task with a generic model that uses only pixel information.
Representations of language in a model of visually grounded speech signal
Authors: Grzegorz Chrupała, Lieke Gelderloos, Afra Alishahi
Date: 2017-02-07 00:00:00
Proceeding: ACL 2017 7
Tasks: Form

We present a visually grounded model of speech perception which projects
spoken utterances and images to a joint semantic space. We use a multi-layer
recurrent highway network to model the temporal nature of spoken speech, and
show that it learns to extract both form and meaning-based linguistic knowledge
from the input signal. We carry out an in-depth analysis of the representations
used by different components of the trained model and show that encoding of
semantic aspects tends to become richer as we go up the hierarchy of layers,
whereas encoding of form-related aspects of the language input tends to
initially increase and then plateau or decrease.
Statistical mechanics of the inverse Ising problem and the optimal objective function
Authors: Johannes Berg
Date: 2016-11-14 00:00:00

The inverse Ising problem seeks to reconstruct the parameters of an Ising
Hamiltonian on the basis of spin configurations sampled from the Boltzmann
measure. Over the last decade, many applications of the inverse Ising problem
have arisen, driven by the advent of large-scale data across different
scientific disciplines. Recently, strategies to solve the inverse Ising problem
based on convex optimisation have proven to be very successful. These
approaches maximise particular objective functions with respect to the model
parameters. Examples are the pseudolikelihood method and interaction screening.
In this paper, we establish a link between approaches to the inverse Ising
problem based on convex optimisation and the statistical physics of disordered
systems. We characterise the performance of an arbitrary objective function and
calculate the objective function which optimally reconstructs the model
parameters. We evaluate the optimal objective function within a
replica-symmetric ansatz and compare the results of the optimal objective
function with other reconstruction methods. Apart from giving a theoretical
underpinning to solving the inverse Ising problem by convex optimisation, the
optimal objective function outperforms state-of-the-art methods, albeit by a
small margin.
Gamblets for opening the complexity-bottleneck of implicit schemes for hyperbolic and parabolic ODEs/PDEs with rough coefficients
Authors: Houman Owhadi, Lei Zhang
Date: 2016-06-24 00:00:00

Implicit schemes are popular methods for the integration of time dependent
PDEs such as hyperbolic and parabolic PDEs. However the necessity to solve
corresponding linear systems at each time step constitutes a complexity
bottleneck in their application to PDEs with rough coefficients. We present a
generalization of gamblets introduced in \cite{OwhadiMultigrid:2015} enabling
the resolution of these implicit systems in near-linear complexity and provide
rigorous a-priori error bounds on the resulting numerical approximations of
hyperbolic and parabolic PDEs. These generalized gamblets induce a
multiresolution decomposition of the solution space that is adapted to both the
underlying (hyperbolic and parabolic) PDE (and the system of ODEs resulting
from space discretization) and to the time-steps of the numerical scheme.