pwc-archive/papers-with-abstracts huggingface dataset:
A multiobjective deep learning approach for predictive classification in Neuroblastoma
Authors: Valerio Maggio, Marco Chierici, Giuseppe Jurman, Cesare Furlanello
Date: 2017-11-22 00:00:00
Tasks: Classification, Deep Learning, Diagnostic, General Classification, Prognosis

Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical
courses that may vary from spontaneous regression to fatal progression; an
accurate patient's risk estimation at diagnosis is essential to design
appropriate tumor treatment strategies. Neuroblastoma is a paradigm disease
where different diagnostic and prognostic endpoints should be predicted from
common molecular and clinical information, with increasing complexity, as shown
in the FDA MAQC-II study. Here we introduce the novel multiobjective deep
learning architecture CDRP (Concatenated Diagnostic Relapse Prognostic)
composed by 8 layers to obtain a combined diagnostic and prognostic prediction
from high-throughput transcriptomics data. Two distinct loss functions are
optimized for the Event Free Survival (EFS) and Overall Survival (OS)
prognosis, respectively. We use the High-Risk (HR) diagnostic information as an
additional input generated by an autoencoder embedding. The latter is used as
network regulariser, based on a clinical algorithm commonly adopted for
stratifying patients from cancer stage, age at insurgence of disease, and MYCN,
the specific molecular marker. The architecture was applied to Illumina
HiSeq2000 RNA-Seq for 498 neuroblastoma patients (176 at high risk) from the
Sequencing Quality Control (SEQC) study, obtaining state-of-art on the
diagnostic endpoint and improving prediction of prognosis over the HR cohort.
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Authors: Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, Dawn Song
Date: 2018-02-22 00:00:00

This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization. In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. Specifically, for models trained without consideration of memorization, we describe new, efficient procedures that can extract unique, secret sequences, such as credit card numbers. We show that our testing strategy is a practical and easy-to-use first line of defense, e.g., by describing its application to quantitatively limit data exposure in Google's Smart Compose, a commercial text-completion neural network trained on millions of users' email messages.
A new model for Cerebellar computation
Authors: Reza Moazzezi
Date: 2018-02-22 00:00:00
Tasks: model

The standard state space model is widely believed to account for the
cerebellar computation in motor adaptation tasks [1]. Here we show that several
recent experiments [2-4] where the visual feedback is irrelevant to the motor
response challenge the standard model. Furthermore, we propose a new model that
accounts for the the results presented in [2-4]. According to this new model,
learning and forgetting are coupled and are error size dependent. We also show
that under reasonable assumptions, our proposed model is the only model that
accounts for both the classical adaptation paradigm as well as the recent
experiments [2-4].
ChatPainter: Improving Text to Image Generation using Dialogue
Authors: Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, Yoshua Bengio
Date: 2018-02-22 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Synthesizing realistic images from text descriptions on a dataset like
Microsoft Common Objects in Context (MS COCO), where each image can contain
several objects, is a challenging task. Prior work has used text captions to
generate images. However, captions might not be informative enough to capture
the entire image and insufficient for the model to be able to understand which
objects in the images correspond to which words in the captions. We show that
adding a dialogue that further describes the scene leads to significant
improvement in the inception score and in the quality of generated images on
the MS COCO dataset.
A Polynomial Time Subsumption Algorithm for Nominal Safe $\mathcal{ELO}_\bot$ under Rational Closure
Authors: Giovanni Casini, Umberto Straccia, Thomas Meyer
Date: 2018-02-22 00:00:00

Description Logics (DLs) under Rational Closure (RC) is a well-known
framework for non-monotonic reasoning in DLs. In this paper, we address the
concept subsumption decision problem under RC for nominal safe
$\mathcal{ELO}_\bot$, a notable and practically important DL representative of
the OWL 2 profile OWL 2 EL.
  Our contribution here is to define a polynomial time subsumption procedure
for nominal safe $\mathcal{ELO}_\bot$ under RC that relies entirely on a series
of classical, monotonic $\mathcal{EL}_\bot$ subsumption tests. Therefore, any
existing classical monotonic $\mathcal{EL}_\bot$ reasoner can be used as a
black box to implement our method. We then also adapt the method to one of the
known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without
losing the computational tractability.
A Closed-form Solution to Photorealistic Image Stylization
Authors: Yijun Li, Ming-Yu Liu, Xueting Li, Ming-Hsuan Yang, Jan Kautz
Date: 2018-02-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Form, Image Stylization

Photorealistic image stylization concerns transferring style of a reference
photo to a content photo with the constraint that the stylized photo should
remain photorealistic. While several photorealistic image stylization methods
exist, they tend to generate spatially inconsistent stylizations with
noticeable artifacts. In this paper, we propose a method to address these
issues. The proposed method consists of a stylization step and a smoothing
step. While the stylization step transfers the style of the reference photo to
the content photo, the smoothing step ensures spatially consistent
stylizations. Each of the steps has a closed-form solution and can be computed
efficiently. We conduct extensive experimental validations. The results show
that the proposed method generates photorealistic stylization outputs that are
more preferred by human subjects as compared to those by the competing methods
while running much faster. Source code and additional results are available at
https://github.com/NVIDIA/FastPhotoStyle .
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery
Authors: Tiep Vu, Lam Nguyen, Tiantong Guo, Vishal Monga
Date: 2018-01-16 00:00:00
Tasks: Classification, Denoising, General Classification, Sparse Representation-based Classification

Classifying buried and obscured targets of interest from other natural and
manmade clutter objects in the scene is an important problem for the U.S. Army.
Targets of interest are often represented by signals captured using
low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar
(SAR) technology. This technology has been used in various applications,
including ground penetration and sensing-through-the-wall. However, the
technology still faces a significant issues regarding low-resolution SAR
imagery in this particular frequency band, low radar cross sections (RCS),
small objects compared to radar signal wavelengths, and heavy interference. The
classification problem has been firstly, and partially, addressed by sparse
representation-based classification (SRC) method which can extract noise from
signals and exploit the cross-channel information. Despite providing potential
results, SRC-related methods have drawbacks in representing nonlinear relations
and dealing with larger training sets. In this paper, we propose a Simultaneous
Decomposition and Classification Network (SDCN) to alleviate noise inferences
and enhance classification accuracy. The network contains two jointly trained
sub-networks: the decomposition sub-network handles denoising, while the
classification sub-network discriminates targets from confusers. Experimental
results show significant improvements over a network without decomposition and
SRC-related methods.
Learning Causally-Generated Stationary Time Series
Authors: Wessel Bruinsma, Richard E. Turner
Date: 2018-02-22 00:00:00
Tasks: Time Series, Time Series Analysis, Variational Inference

We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly
nonparametric model for causal, spectrally complex dynamical phenomena. The
CGPCM is a generative model in which white noise is passed through a causal,
nonparametric-window moving-average filter, a construction that we show to be
equivalent to a Gaussian process with a nonparametric kernel that is biased
towards causally-generated signals. We develop enhanced variational inference
and learning schemes for the CGPCM and its previous acausal variant, the GPCM
(Tobar et al., 2015b), that significantly improve statistical accuracy. These
modelling and inferential contributions are demonstrated on a range of
synthetic and real-world signals.
An Analysis of Categorical Distributional Reinforcement Learning
Authors: Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
Date: 2018-02-22 00:00:00
Tasks: Distributional Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Distributional approaches to value-based reinforcement learning model the
entire distribution of returns, rather than just their expected values, and
have recently been shown to yield state-of-the-art empirical performance. This
was demonstrated by the recently proposed C51 algorithm, based on categorical
distributional reinforcement learning (CDRL) [Bellemare et al., 2017]. However,
the theoretical properties of CDRL algorithms are not yet well understood. In
this paper, we introduce a framework to analyse CDRL algorithms, establish the
importance of the projected distributional Bellman operator in distributional
RL, draw fundamental connections between CDRL and the Cram\'er distance, and
give a proof of convergence for sample-based categorical distributional
reinforcement learning algorithms.
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data
Authors: Diego Moussallem, Thiago castro Ferreira, Marcos Zampieri, Maria Claudia Cavalcanti, Geraldo Xexéo, Mariana Neves, Axel-Cyrille Ngonga Ngomo
Date: 2018-02-22 00:00:00
Proceeding: LREC 2018 5

The generation of natural language from Resource Description Framework (RDF)
data has recently gained significant attention due to the continuous growth of
Linked Data. A number of these approaches generate natural language in
languages other than English, however, no work has been proposed to generate
Brazilian Portuguese texts out of RDF. We address this research gap by
presenting RDF2PT, an approach that verbalizes RDF data to Brazilian Portuguese
language. We evaluated RDF2PT in an open questionnaire with 44 native speakers
divided into experts and non-experts. Our results suggest that RDF2PT is able
to generate text which is similar to that generated by humans and can hence be
easily understood.
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Authors: Masaaki Takada, Taiji Suzuki, Hironori Fujisawa
Date: 2017-11-06 00:00:00
Tasks: regression

Sparse regularization such as $\ell_1$ regularization is a quite powerful and
widely used strategy for high dimensional learning problems. The effectiveness
of sparse regularization has been supported practically and theoretically by
several studies. However, one of the biggest issues in sparse regularization is
that its performance is quite sensitive to correlations between features.
Ordinary $\ell_1$ regularization can select variables correlated with each
other, which results in deterioration of not only its generalization error but
also interpretability. In this paper, we propose a new regularization method,
"Independently Interpretable Lasso" (IILasso). Our proposed regularizer
suppresses selecting correlated variables, and thus each active variable
independently affects the objective variable in the model. Hence, we can
interpret regression coefficients intuitively and also improve the performance
by avoiding overfitting. We analyze theoretical property of IILasso and show
that the proposed method is much advantageous for its sign recovery and
achieves almost minimax optimal convergence rate. Synthetic and real data
analyses also indicate the effectiveness of IILasso.
LIDIOMS: A Multilingual Linked Idioms Data Set
Authors: Diego Moussallem, Mohamed Ahmed Sherif, Diego Esteves, Marcos Zampieri, Axel-Cyrille Ngonga Ngomo
Date: 2018-02-22 00:00:00
Proceeding: LREC 2018 5

In this paper, we describe the LIDIOMS data set, a multilingual RDF
representation of idioms currently containing five languages: English, German,
Italian, Portuguese, and Russian. The data set is intended to support natural
language processing applications by providing links between idioms across
languages. The underlying data was crawled and integrated from various sources.
To ensure the quality of the crawled data, all idioms were evaluated by at
least two native speakers. Herein, we present the model devised for structuring
the data. We also provide the details of linking LIDIOMS to well-known
multilingual data sets such as BabelNet. The resulting data set complies with
best practices according to Linguistic Linked Open Data Community.
Path-Specific Counterfactual Fairness
Authors: Silvia Chiappa, Thomas P. S. Gillam
Date: 2018-02-22 00:00:00
Tasks: Attribute, counterfactual, Fairness

We consider the problem of learning fair decision systems in complex
scenarios in which a sensitive attribute might affect the decision along both
fair and unfair pathways. We introduce a causal approach to disregard effects
along unfair pathways that simplifies and generalizes previous literature. Our
method corrects observations adversely affected by the sensitive attribute, and
uses these to form a decision. This avoids disregarding fair information, and
does not require an often intractable computation of the path-specific effect.
We leverage recent developments in deep learning and approximate inference to
achieve a solution that is widely applicable to complex, non-linear scenarios.
Reliable Intersection Control in Non-cooperative Environments
Authors: Muhammed O. Sayin, Chung-Wei Lin, Shinichi Shiraishi, Tamer Başar
Date: 2018-02-22 00:00:00

We propose a reliable intersection control mechanism for strategic autonomous
and connected vehicles (agents) in non-cooperative environments. Each agent has
access to his/her earliest possible and desired passing times, and reports a
passing time to the intersection manager, who allocates the intersection
temporally to the agents in a First-Come-First-Serve basis. However, the agents
might have conflicting interests and can take actions strategically. To this
end, we analyze the strategic behaviors of the agents and formulate Nash
equilibria for all possible scenarios. Furthermore, among all Nash equilibria
we identify a socially optimal equilibrium that leads to a fair intersection
allocation, and correspondingly we describe a strategy-proof intersection
mechanism, which achieves reliable intersection control such that the strategic
agents do not have any incentive to misreport their passing times
strategically.
Pattern-Based Approach to the Workflow Satisfiability Problem with User-Independent Constraints
Authors: Daniel Karapetyan, Andrew J. Parkes, Gregory Gutin, Andrei Gagarin
Date: 2016-04-19 00:00:00

The fixed parameter tractable (FPT) approach is a powerful tool in tackling computationally hard problems. In this paper, we link FPT results to classic artificial intelligence (AI) techniques to show how they complement each other. Specifically, we consider the workflow satisfiability problem (WSP) which asks whether there exists an assignment of authorised users to the steps in a workflow specification, subject to certain constraints on the assignment. It was shown by Cohen et al. (JAIR 2014) that WSP restricted to the class of user-independent constraints (UI), covering many practical cases, admits FPT algorithms, i.e. can be solved in time exponential only in the number of steps $k$ and polynomial in the number of users $n$. Since usually $k << n$ in WSP, such FPT algorithms are of great practical interest. We present a new interpretation of the FPT nature of the WSP with UI constraints giving a decomposition of the problem into two levels. Exploiting this two-level split, we develop a new FPT algorithm that is by many orders of magnitude faster than the previous state-of-the-art WSP algorithm and also has only polynomial-space complexity. We also introduce new pseudo-Boolean (PB) and Constraint Satisfaction (CSP) formulations of the WSP with UI constraints which efficiently exploit this new decomposition of the problem and raise the novel issue of how to use general-purpose solvers to tackle FPT problems in a fashion that meets FPT efficiency expectations. In our computational study, we investigate, for the first time, the phase transition (PT) properties of the WSP, under a model for generation of random instances. We show how PT studies can be extended, in a novel fashion, to support empirical evaluation of scaling of FPT algorithms.
Harmonious Attention Network for Person Re-Identification
Authors: Wei Li, Xiatian Zhu, Shaogang Gong
Date: 2018-02-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification

Existing person re-identification (re-id) methods either assume the
availability of well-aligned person bounding box images as model input or rely
on constrained attention selection mechanisms to calibrate misaligned images.
They are therefore sub-optimal for re-id matching in arbitrarily aligned person
images potentially with large human pose variations and unconstrained
auto-detection errors. In this work, we show the advantages of jointly learning
attention selection and feature representation in a Convolutional Neural
Network (CNN) by maximising the complementary information of different levels
of visual attention subject to re-id discriminative learning constraints.
Specifically, we formulate a novel Harmonious Attention CNN (HA-CNN) model for
joint learning of soft pixel attention and hard regional attention along with
simultaneous optimisation of feature representations, dedicated to optimise
person re-id in uncontrolled (misaligned) images. Extensive comparative
evaluations validate the superiority of this new HA-CNN model for person re-id
over a wide variety of state-of-the-art methods on three large-scale benchmarks
including CUHK03, Market-1501, and DukeMTMC-ReID.
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
Authors: Sanket Kamthe, Marc Peter Deisenroth
Date: 2017-06-20 00:00:00
Tasks: Gaussian Processes, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Trial-and-error based reinforcement learning (RL) has seen rapid advancements
in recent times, especially with the advent of deep neural networks. However,
the majority of autonomous RL algorithms require a large number of interactions
with the environment. A large number of interactions may be impractical in many
real-world applications, such as robotics, and many practical systems have to
obey limitations in the form of state space or control constraints. To reduce
the number of system interactions while simultaneously handling constraints, we
propose a model-based RL framework based on probabilistic Model Predictive
Control (MPC). In particular, we propose to learn a probabilistic transition
model using Gaussian Processes (GPs) to incorporate model uncertainty into
long-term predictions, thereby, reducing the impact of model errors. We then
use MPC to find a control sequence that minimises the expected long-term cost.
We provide theoretical guarantees for first-order optimality in the GP-based
transition models with deterministic approximate inference for long-term
planning. We demonstrate that our approach does not only achieve
state-of-the-art data efficiency, but also is a principled way for RL in
constrained environments.
A Deep Learning Approach for Privacy Preservation in Assisted Living
Authors: Ismini Psychoula, Erinc Merdivan, Deepika Singh, Liming Chen, Feng Chen, Sten Hanke, Johannes Kropf, Andreas Holzinger, Matthieu Geist
Date: 2018-02-22 00:00:00
Tasks: Decoder, Deep Learning

In the era of Internet of Things (IoT) technologies the potential for privacy
invasion is becoming a major concern especially in regards to healthcare data
and Ambient Assisted Living (AAL) environments. Systems that offer AAL
technologies make extensive use of personal data in order to provide services
that are context-aware and personalized. This makes privacy preservation a very
important issue especially since the users are not always aware of the privacy
risks they could face. A lot of progress has been made in the deep learning
field, however, there has been lack of research on privacy preservation of
sensitive personal data with the use of deep learning. In this paper we focus
on a Long Short Term Memory (LSTM) Encoder-Decoder, which is a principal
component of deep learning, and propose a new encoding technique that allows
the creation of different AAL data views, depending on the access level of the
end user and the information they require access to. The efficiency and
effectiveness of the proposed method are demonstrated with experiments on a
simulated AAL dataset. Qualitatively, we show that the proposed model learns
privacy operations such as disclosure, deletion and generalization and can
perform encoding and decoding of the data with almost perfect recovery.
Deep Learning for Video Classification and Captioning
Authors: Zuxuan Wu, Ting Yao, Yanwei Fu, Yu-Gang Jiang
Date: 2016-09-22 00:00:00
Tasks: Classification, Deep Learning, General Classification, Sentence, Video Captioning, Video Classification

Accelerated by the tremendous increase in Internet bandwidth and storage
space, video data has been generated, published and spread explosively,
becoming an indispensable part of today's big data. In this paper, we focus on
reviewing two lines of research aiming to stimulate the comprehension of videos
with deep learning: video classification and video captioning. While video
classification concentrates on automatically labeling video clips based on
their semantic contents like human actions or complex events, video captioning
attempts to generate a complete and natural sentence, enriching the single
label as in video classification, to capture the most informative dynamics in
videos. In addition, we also provide a review of popular benchmarks and
competitions, which are critical for evaluating the technical progress of this
vibrant field.