pwc-archive/papers-with-abstracts huggingface dataset:
Surrogate Outcomes and Transportability
Authors: Santtu Tikka, Juha Karvanen
Date: 2018-06-19 00:00:00
Tasks: Causal Inference

Identification of causal effects is one of the most fundamental tasks of
causal inference. We consider an identifiability problem where some
experimental and observational data are available but neither data alone is
sufficient for the identification of the causal effect of interest. Instead of
the outcome of interest, surrogate outcomes are measured in the experiments.
This problem is a generalization of identifiability using surrogate experiments
and we label it as surrogate outcome identifiability. We show that the concept
of transportability provides a sufficient criteria for determining surrogate
outcome identifiability for a large class of queries.
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings
Authors: Anguelos Nicolaou, Sounak Dey, Vincent Christlein, Andreas Maier, Dimosthenis Karatzas
Date: 2018-06-19 00:00:00
Tasks: Computer Security

Embedding data into vector spaces is a very popular strategy of pattern
recognition methods. When distances between embeddings are quantized,
performance metrics become ambiguous. In this paper, we present an analysis of
the ambiguity quantized distances introduce and provide bounds on the effect.
We demonstrate that it can have a measurable effect in empirical data in
state-of-the-art systems. We also approach the phenomenon from a computer
security perspective and demonstrate how someone being evaluated by a third
party can exploit this ambiguity and greatly outperform a random predictor
without even access to the input data. We also suggest a simple solution making
the performance metrics, which rely on ranking, totally deterministic and
impervious to such exploits.
Learning from Chunk-based Feedback in Neural Machine Translation
Authors: Pavel Petrushkov, Shahram Khadivi, Evgeny Matusov
Date: 2018-06-19 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, Sentence, Translation

We empirically investigate learning from partial feedback in neural machine
translation (NMT), when partial feedback is collected by asking users to
highlight a correct chunk of a translation. We propose a simple and effective
way of utilizing such feedback in NMT training. We demonstrate how the common
machine translation problem of domain mismatch between training and deployment
can be reduced solely based on chunk-level user feedback. We conduct a series
of simulation experiments to test the effectiveness of the proposed method. Our
results show that chunk-level feedback outperforms sentence based feedback by
up to 2.61% BLEU absolute.
Approximation Strategies for Incomplete MaxSAT
Authors: Saurabh Joshi, Prateek Kumar, Ruben Martins, Sukrut Rao
Date: 2018-06-19 00:00:00

Incomplete MaxSAT solving aims to quickly find a solution that attempts to
minimize the sum of the weights of the unsatisfied soft clauses without
providing any optimality guarantees.
  In this paper, we propose two approximation strategies for improving
incomplete MaxSAT solving. In one of the strategies, we cluster the weights and
approximate them with a representative weight. In another strategy, we break up
the problem of minimizing the sum of weights of unsatisfiable clauses into
multiple minimization subproblems. Experimental results show that approximation
strategies can be used to find better solutions than the best incomplete
solvers in the MaxSAT Evaluation 2017.
Semi-supervised Hashing for Semi-Paired Cross-View Retrieval
Authors: Jun Yu, Xiao-Jun Wu, Josef Kittler
Date: 2018-06-19 00:00:00
Tasks: Retrieval

Recently, hashing techniques have gained importance in large-scale retrieval
tasks because of their retrieval speed. Most of the existing cross-view
frameworks assume that data are well paired. However, the fully-paired
multiview situation is not universal in real applications. The aim of the
method proposed in this paper is to learn the hashing function for semi-paired
cross-view retrieval tasks. To utilize the label information of partial data,
we propose a semi-supervised hashing learning framework which jointly performs
feature extraction and classifier learning. The experimental results on two
datasets show that our method outperforms several state-of-the-art methods in
terms of retrieval accuracy.
Automatic segmentation of prostate zones
Authors: Germonda Mooij, Ines Bagulho, Henkjan Huisman
Date: 2018-06-19 00:00:00
Tasks: Anatomy, Medical Image Analysis, Segmentation

Convolutional networks have become state-of-the-art techniques for automatic
medical image analysis, with the U-net architecture being the most popular at
this moment. In this article we report the application of a 3D version of U-net
to the automatic segmentation of prostate peripheral and transition zones in 3D
MRI images. Our results are slightly better than recent studies that used 2D
U-net and handcrafted feature approaches.
  In addition, we test ideas for improving the 3D U-net setup, by 1) letting
the network segment surrounding tissues, making use of the fixed anatomy, and
2) adjusting the network architecture to reflect the anisotropy in the
dimensions of the MRI image volumes. While the latter adjustment gave a
marginal improvement, the former adjustment showed a significant deterioration
of the network performance. We were able to explain this deterioration by
inspecting feature map activations in all layers of the network. We show that
to segment more tissues the network replaces feature maps that were dedicated
to detecting prostate peripheral zones, by feature maps detecting the
surrounding tissues.
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models
Authors: Henry B. Moss, David S. Leslie, Paul Rayson
Date: 2018-06-19 00:00:00
Tasks: Document Classification, General Classification, Model Selection, Part-Of-Speech Tagging, Sentiment Analysis, Sentiment Classification

K-fold cross validation (CV) is a popular method for estimating the true
performance of machine learning models, allowing model selection and parameter
tuning. However, the very process of CV requires random partitioning of the
data and so our performance estimates are in fact stochastic, with variability
that can be substantial for natural language processing tasks. We demonstrate
that these unstable estimates cannot be relied upon for effective parameter
tuning. The resulting tuned parameters are highly sensitive to how our data is
partitioned, meaning that we often select sub-optimal parameter choices and
have serious reproducibility issues.
  Instead, we propose to use the less variable J-K-fold CV, in which J
independent K-fold cross validations are used to assess performance. Our main
contributions are extending J-K-fold CV from performance estimation to
parameter tuning and investigating how to choose J and K. We argue that
variability is more important than bias for effective tuning and so advocate
lower choices of K than are typically seen in the NLP literature, instead use
the saved computation to increase J. To demonstrate the generality of our
recommendations we investigate a wide range of case-studies: sentiment
classification (both general and target-specific), part-of-speech tagging and
document classification.
Large-Scale Stochastic Sampling from the Probability Simplex
Authors: Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth
Date: 2018-06-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference, Topic Models

Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular
method for scalable Bayesian inference. These methods are based on sampling a
discrete-time approximation to a continuous time process, such as the Langevin
diffusion. When applied to distributions defined on a constrained space the
time-discretization error can dominate when we are near the boundary of the
space. We demonstrate that because of this, current SGMCMC methods for the
simplex struggle with sparse simplex spaces; when many of the components are
close to zero. Unfortunately, many popular large-scale Bayesian models, such as
network or topic models, require inference on sparse simplex spaces. To avoid
the biases caused by this discretization error, we propose the stochastic
Cox-Ingersoll-Ross process (SCIR), which removes all discretization error and
we prove that samples from the SCIR process are asymptotically unbiased. We
discuss how this idea can be extended to target other constrained spaces. Use
of the SCIR process within a SGMCMC algorithm is shown to give substantially
better performance for a topic model and a Dirichlet process mixture model than
existing SGMCMC approaches.
SMarTplan: a Task Planner for Smart Factories
Authors: Arthur Bit-Monnot, Francesco Leofante, Luca Pulina, Erika Abraham, Armando Tacchella
Date: 2018-06-19 00:00:00
Tasks: Task Planning

Smart factories are on the verge of becoming the new industrial paradigm,
wherein optimization permeates all aspects of production, from concept
generation to sales. To fully pursue this paradigm, flexibility in the
production means as well as in their timely organization is of paramount
importance. AI is planning a major role in this transition, but the scenarios
encountered in practice might be challenging for current tools. Task planning
is one example where AI enables more efficient and flexible operation through
an online automated adaptation and rescheduling of the activities to cope with
new operational constraints and demands.
  In this paper we present SMarTplan, a task planner specifically conceived to
deal with real-world scenarios in the emerging smart factory paradigm.
Including both special-purpose and general-purpose algorithms, SMarTplan is
based on current automated reasoning technology and it is designed to tackle
complex application domains. In particular, we show its effectiveness on a
logistic scenario, by comparing its specialized version with the general
purpose one, and extending the comparison to other state-of-the-art task
planners.
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans
Authors: Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne
Date: 2018-06-19 00:00:00
Tasks: Medical Image Analysis

Supervised feature learning using convolutional neural networks (CNNs) can
provide concise and disease relevant representations of medical images.
However, training CNNs requires annotated image data. Annotating medical images
can be a time-consuming task and even expert annotations are subject to
substantial inter- and intra-rater variability. Assessing visual similarity of
images instead of indicating specific pathologies or estimating disease
severity could allow non-experts to participate, help uncover new patterns, and
possibly reduce rater variability. We consider the task of assessing emphysema
extent in chest CT scans. We derive visual similarity triplets from visually
assessed emphysema extent and learn a low dimensional embedding using CNNs. We
evaluate the networks on 973 images, and show that the CNNs can learn disease
relevant feature representations from derived similarity triplets. To our
knowledge this is the first medical image application where similarity triplets
has been used to learn a feature representation that can be used for embedding
unseen test images
Instance-Level Explanations for Fraud Detection: A Case Study
Authors: Dennis Collaris, Leo M. Vink, Jarke J. van Wijk
Date: 2018-06-19 00:00:00
Tasks: Fraud Detection, Prediction

Fraud detection is a difficult problem that can benefit from predictive
modeling. However, the verification of a prediction is challenging; for a
single insurance policy, the model only provides a prediction score. We present
a case study where we reflect on different instance-level model explanation
techniques to aid a fraud detection team in their work. To this end, we
designed two novel dashboards combining various state-of-the-art explanation
techniques. These enable the domain expert to analyze and understand
predictions, dramatically speeding up the process of filtering potential fraud
cases. Finally, we discuss the lessons learned and outline open research
issues.
FineTag: Multi-attribute Classification at Fine-grained Level in Images
Authors: Roshanak Zakizadeh, Michele Sasdelli, Yu Qian, Eduard Vazquez
Date: 2018-06-19 00:00:00
Tasks: Attribute, Classification, General Classification

In this paper, we address the extraction of the fine-grained attributes of an
instance as a `multi-attribute classification' problem. To this end, we propose
an end-to-end architecture by adopting the bi-linear Convolutional Neural
Network with the pairwise ranking loss. This is the first time such
architecture is applied for the fine-grained attributes classification problem.
We compared the proposed method with a competitive deep Convolutional Neural
Network baseline. Extensive experiments show that the proposed method
attains/outperforms the performance of compared baseline with significantly
less number of parameters ($40\times$ less). We demonstrated our approach on
CUB200 birds dataset whose annotations are adapted in this work for
multi-attribute classification at fine-grained level.
Infrared and Visible Image Fusion with ResNet and zero-phase component analysis
Authors: Hui Li, Xiao-Jun Wu, Tariq S. Durrani
Date: 2018-06-19 00:00:00
Tasks: Infrared And Visible Image Fusion

Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken. By contrast, most of deep learning-based methods use deep features directly without feature extraction or processing. This leads to the fusion performance degradation in some cases. To solve these drawbacks, we propose a deep features and zero-phase component analysis (ZCA) based novel fusion framework is this paper. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA is utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed framework achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https://github.com/hli1221/imagefusion_resnet50
Facing Multiple Attacks in Adversarial Patrolling Games with Alarmed Targets
Authors: Giuseppe De Nittis, Nicola Gatti
Date: 2018-06-19 00:00:00

We focus on adversarial patrolling games on arbitrary graphs, where the
Defender can control a mobile resource, the targets are alarmed by an alarm
system, and the Attacker can observe the actions of the mobile resource of the
Defender and perform different attacks exploiting multiple resources. This
scenario can be modeled as a zero-sum extensive-form game in which each player
can play multiple times. The game tree is exponentially large both in the size
of the graph and in the number of attacking resources. We show that when the
number of the Attacker's resources is free, the problem of computing the
equilibrium path is NP-hard, while when the number of resources is fixed, the
equilibrium path can be computed in poly-time. We provide a dynamic-programming
algorithm that, given the number of the Attacker's resources, computes the
equilibrium path requiring poly-time in the size of the graph and exponential
time in the number of the resources. Furthermore, since in real-world scenarios
it is implausible that the Defender knows the number of attacking resources, we
study the robustness of the Defender's strategy when she makes a wrong guess
about that number. We show that even the error of just a single resource can
lead to an arbitrary inefficiency, when the inefficiency is defined as the
ratio of the Defender's utilities obtained with a wrong guess and a correct
guess. However, a more suitable definition of inefficiency is given by the
difference of the Defender's utilities: this way, we observe that the higher
the error in the estimation, the higher the loss for the Defender. Then, we
investigate the performance of online algorithms when no information about the
Attacker's resources is available. Finally, we resort to randomized online
algorithms showing that we can obtain a competitive factor that is twice better
than the one that can be achieved by any deterministic online algorithm.
Modality Distillation with Multiple Stream Networks for Action Recognition
Authors: Nuno Garcia, Pietro Morerio, Vittorio Murino
Date: 2018-06-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Detection, Action Recognition, Hallucination, Temporal Action Localization

Diverse input data modalities can provide complementary cues for several
tasks, usually leading to more robust algorithms and better performance.
However, while a (training) dataset could be accurately designed to include a
variety of sensory inputs, it is often the case that not all modalities could
be available in real life (testing) scenarios, where a model has to be
deployed. This raises the challenge of how to learn robust representations
leveraging multimodal data in the training stage, while considering limitations
at test time, such as noisy or missing modalities.
  This paper presents a new approach for multimodal video action recognition,
developed within the unified frameworks of distillation and privileged
information, named generalized distillation. Particularly, we consider the case
of learning representations from depth and RGB videos, while relying on RGB
data only at test time. We propose a new approach to train an hallucination
network that learns to distill depth features through multiplicative
connections of spatiotemporal representations, leveraging soft labels and hard
labels, as well as distance between feature maps. We report state-of-the-art
results on video action classification on the largest multimodal dataset
available for this task, the NTU RGB+D. Code available at
https://github.com/ncgarcia/modality-distillation .
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Authors: Yaël Balbastre, Mikael Brudfors, Kevin Bronik, John Ashburner
Date: 2018-06-19 00:00:00
Tasks: Dimensionality Reduction

Shape modelling describes methods aimed at capturing the natural variability
of shapes and commonly relies on probabilistic interpretations of
dimensionality reduction techniques such as principal component analysis. Due
to their computational complexity when dealing with dense deformation models
such as diffeomorphisms, previous attempts have focused on explicitly reducing
their dimension, diminishing de facto their flexibility and ability to model
complex shapes such as brains. In this paper, we present a generative model of
shape that allows the covariance structure of deformations to be captured
without squashing their domain, resulting in better normalisation. An efficient
inference scheme based on Gauss-Newton optimisation is used, which enables
processing of 3D neuroimaging data. We trained this algorithm on segmented
brains from the OASIS database, generating physiologically meaningful
deformation trajectories. To prove the model's robustness, we applied it to
unseen data, which resulted in equivalent fitting scores.
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks
Authors: Qiqi Zhang, Ying Liu
Date: 2018-06-19 00:00:00
Tasks: Brain Computer Interface, Data Augmentation, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

One of the big restrictions in brain computer interface field is the very
limited training samples, it is difficult to build a reliable and usable system
with such limited data. Inspired by generative adversarial networks, we propose
a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks
method to generate more artificial EEG signal automatically for data
augmentation to improve the performance of convolutional neural networks in
brain computer interface field and overcome the small training dataset
problems. We evaluate the proposed cDCGAN method on BCI competition dataset of
motor imagery. The results show that the generated artificial EEG data from
Gaussian noise can learn the features from raw EEG data and has no less than
the classification accuracy of raw EEG data in the testing dataset. Also by
using generated artificial data can effectively improve classification accuracy
at the same model with limited training data.
Online Linear Quadratic Control
Authors: Alon Cohen, Avinatan Hassidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar
Date: 2018-06-19 00:00:00
Proceeding: ICML 2018 7

We study the problem of controlling linear time-invariant systems with known
noisy dynamics and adversarially chosen quadratic losses. We present the first
efficient online learning algorithms in this setting that guarantee
$O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our
algorithms rely on a novel SDP relaxation for the steady-state distribution of
the system. Crucially, and in contrast to previously proposed relaxations, the
feasible solutions of our SDP all correspond to "strongly stable" policies that
mix exponentially fast to a steady state.
End-to-End Speech Recognition From the Raw Waveform
Authors: Neil Zeghidour, Nicolas Usunier, Gabriel Synnaeve, Ronan Collobert, Emmanuel Dupoux
Date: 2018-06-19 00:00:00
Tasks: speech-recognition, Speech Recognition

State-of-the-art speech recognition systems rely on fixed, hand-crafted
features such as mel-filterbanks to preprocess the waveform before the training
pipeline. In this paper, we study end-to-end systems trained directly from the
raw waveform, building on two alternatives for trainable replacements of
mel-filterbanks that use a convolutional architecture. The first one is
inspired by gammatone filterbanks (Hoshen et al., 2015; Sainath et al, 2015),
and the second one by the scattering transform (Zeghidour et al., 2017). We
propose two modifications to these architectures and systematically compare
them to mel-filterbanks, on the Wall Street Journal dataset. The first
modification is the addition of an instance normalization layer, which greatly
improves on the gammatone-based trainable filterbanks and speeds up the
training of the scattering-based filterbanks. The second one relates to the
low-pass filter used in these approaches. These modifications consistently
improve performances for both approaches, and remove the need for a careful
initialization in scattering-based trainable filterbanks. In particular, we
show a consistent improvement in word error rate of the trainable filterbanks
relatively to comparable mel-filterbanks. It is the first time end-to-end
models trained from the raw signal significantly outperform mel-filterbanks on
a large vocabulary task under clean recording conditions.
Enhancing Identification of Causal Effects by Pruning
Authors: Santtu Tikka, Juha Karvanen
Date: 2018-06-19 00:00:00

Causal models communicate our assumptions about causes and effects in
real-world phe- nomena. Often the interest lies in the identification of the
effect of an action which means deriving an expression from the observed
probability distribution for the interventional distribution resulting from the
action. In many cases an identifiability algorithm may return a complicated
expression that contains variables that are in fact unnecessary. In practice
this can lead to additional computational burden and increased bias or
inefficiency of estimates when dealing with measurement error or missing data.
We present graphical criteria to detect variables which are redundant in
identifying causal effects. We also provide an improved version of a well-known
identifiability algorithm that implements these criteria.