pwc-archive/papers-with-abstracts huggingface dataset:
Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance
Authors: Tony Wang, Tim Tschampel, Emilia Apostolova, Tom Velez
Date: 2019-03-28 00:00:00
Tasks: BIG-bench Machine Learning

In this work, we utilize Machine Learning for early recognition of patients
at high risk of acute respiratory distress syndrome (ARDS), which is critical
for successful prevention strategies for this devastating syndrome. The
difficulty in early ARDS recognition stems from its complex and heterogenous
nature. In this study, we integrate knowledge of the heterogeneity of ARDS
patients into predictive model building. Using MIMIC-III data, we first apply
latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS
population, and then build predictive models on the partitioned data. The
results indicate that significantly improved performances of prediction can be
obtained for two of the three identified sub-phenotypes of ARDS. Experiments
suggests that identifying sub-phenotypes is beneficial for building predictive
model for ARDS.
Nearest-Neighbor Neural Networks for Geostatistics
Authors: Haoyu Wang, Yawen Guan, Brian J. Reich
Date: 2019-03-28 00:00:00
Tasks: Deep Learning, valid

Kriging is the predominant method used for spatial prediction, but relies on
the assumption that predictions are linear combinations of the observations.
Kriging often also relies on additional assumptions such as normality and
stationarity. We propose a more flexible spatial prediction method based on the
Nearest-Neighbor Neural Network (4N) process that embeds deep learning into a
geostatistical model. We show that the 4N process is a valid stochastic process
and propose a series of new ways to construct features to be used as inputs to
the deep learning model based on neighboring information. Our model framework
outperforms some existing state-of-art geostatistical modelling methods for
simulated non-Gaussian data and is applied to a massive forestry dataset.
Many Task Learning with Task Routing
Authors: Gjorgji Strezoski, Nanne van Noord, Marcel Worring
Date: 2019-03-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Multi-Task Learning

Typical multi-task learning (MTL) methods rely on architectural adjustments
and a large trainable parameter set to jointly optimize over several tasks.
However, when the number of tasks increases so do the complexity of the
architectural adjustments and resource requirements. In this paper, we
introduce a method which applies a conditional feature-wise transformation over
the convolutional activations that enables a model to successfully perform a
large number of tasks. To distinguish from regular MTL, we introduce Many Task
Learning (MaTL) as a special case of MTL where more than 20 tasks are performed
by a single model. Our method dubbed Task Routing (TR) is encapsulated in a
layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario
successfully fits hundreds of classification tasks in one model. We evaluate
our method on 5 datasets against strong baselines and state-of-the-art
approaches.
Building Automated Survey Coders via Interactive Machine Learning
Authors: Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
Date: 2019-03-28 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Software systems trained via machine learning to automatically classify
open-ended answers (a.k.a. verbatims) are by now a reality. Still, their
adoption in the survey coding industry has been less widespread than it might
have been. Among the factors that have hindered a more massive takeup of this
technology are the effort involved in manually coding a sufficient amount of
training data, the fact that small studies do not seem to justify this effort,
and the fact that the process needs to be repeated anew when brand new coding
tasks arise. In this paper we will argue for an approach to building verbatim
classifiers that we will call "Interactive Learning", and that addresses all
the above problems. We will show that, for the same amount of training effort,
interactive learning delivers much better coding accuracy than standard
"non-interactive" learning. This is especially true when the amount of data we
are willing to manually code is small, which makes this approach attractive
also for small-scale studies. Interactive learning also lends itself to reusing
previously trained classifiers for dealing with new (albeit related) coding
tasks. Interactive learning also integrates better in the daily workflow of the
survey specialist, and delivers a better user experience overall.
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning
Authors: Ashwinkumar Ganesan, Pooja Parameshwarappa, Akshay Peshave, ZhiYuan Chen, Tim Oates
Date: 2019-03-28 00:00:00
Tasks: Intrusion Detection

Evolving cybersecurity threats are a persistent challenge for
systemadministrators and security experts as new malwares are continu-ally
released. Attackers may look for vulnerabilities in commercialproducts or
execute sophisticated reconnaissance campaigns tounderstand a targets network
and gather information on securityproducts like firewalls and intrusion
detection / prevention systems(network or host-based). Many new attacks tend to
be modificationsof existing ones. In such a scenario, rule-based systems fail
to detectthe attack, even though there are minor differences in conditions
/attributes between rules to identify the new and existing attack. Todetect
these differences the IDS must be able to isolate the subset ofconditions that
are true and predict the likely conditions (differentfrom the original) that
must be observed. In this paper, we proposeaprobabilistic abductive
reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to
detect these evolved attacks by (a)Predicting rule conditions that are likely
to occur (based on existingrules) and (b) able to generate new snort rules when
provided withseed rule (i.e. a starting rule) to reduce the burden on experts
toconstantly update them. We demonstrate the effectiveness of theapproach by
generating new rules from the snort 2012 rules set andtesting it on the MACCDC
2012 dataset [6].
Recommendation Systems for Tourism Based on Social Networks: A Survey
Authors: Alan Menk, Laura Sebastia, Rebeca Ferreira
Date: 2019-03-28 00:00:00
Tasks: Recommendation Systems, Survey

Nowadays, recommender systems are present in many daily activities such as
online shopping, browsing social networks, etc. Given the rising demand for
reinvigoration of the tourist industry through information technology,
recommenders have been included into tourism websites such as Expedia, Booking
or Tripadvisor, among others. Furthermore, the amount of scientific papers
related to recommender systems for tourism is on solid and continuous growth
since 2004. Much of this growth is due to social networks that, besides to
offer researchers the possibility of using a great mass of available and
constantly updated data, they also enable the recommendation systems to become
more personalised, effective and natural. This paper reviews and analyses many
research publications focusing on tourism recommender systems that use social
networks in their projects. We detail their main characteristics, like which
social networks are exploited, which data is extracted, the applied
recommendation techniques, the methods of evaluation, etc. Through a
comprehensive literature review, we aim to collaborate with the future
recommender systems, by giving some clear classifications and descriptions of
the current tourism recommender systems.
Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)
Authors: John Gideon, Melvin G McInnis, Emily Mower Provost
Date: 2019-03-28 00:00:00
Tasks: Cross-corpus, Domain Generalization, Emotion Recognition, Speech Emotion Recognition

Automatic speech emotion recognition provides computers with critical context to enable user understanding. While methods trained and tested within the same dataset have been shown successful, they often fail when applied to unseen datasets. To address this, recent work has focused on adversarial methods to find more generalized representations of emotional speech. However, many of these methods have issues converging, and only involve datasets collected in laboratory conditions. In this paper, we introduce Adversarial Discriminative Domain Generalization (ADDoG), which follows an easier to train "meet in the middle" approach. The model iteratively moves representations learned for each dataset closer to one another, improving cross-dataset generalization. We also introduce Multiclass ADDoG, or MADDoG, which is able to extend the proposed method to more than two datasets, simultaneously. Our results show consistent convergence for the introduced methods, with significantly improved results when not using labels from the target dataset. We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered. Even though our experiments focus on cross-corpus speech emotion, these methods could be used to remove unwanted factors of variation in other settings.
Deep Neural Network Embeddings with Gating Mechanisms for Text-Independent Speaker Verification
Authors: Lanhua You, Wu Guo, Li-Rong Dai, Jun Du
Date: 2019-03-28 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

In this paper, gating mechanisms are applied in deep neural network (DNN)
training for x-vector-based text-independent speaker verification. First, a
gated convolution neural network (GCNN) is employed for modeling the
frame-level embedding layers. Compared with the time-delay DNN (TDNN), the GCNN
can obtain more expressive frame-level representations through carefully
designed memory cell and gating mechanisms. Moreover, we propose a novel
gated-attention statistics pooling strategy in which the attention scores are
shared with the output gate. The gated-attention statistics pooling combines
both gating and attention mechanisms into one framework; therefore, we can
capture more useful information in the temporal pooling layer. Experiments are
carried out using the NIST SRE16 and SRE18 evaluation datasets. The results
demonstrate the effectiveness of the GCNN and show that the proposed
gated-attention statistics pooling can further improve the performance.
Learning to Weight for Text Classification
Authors: Alejandro Moreo Fernández, Andrea Esuli, Fabrizio Sebastiani
Date: 2019-03-28 00:00:00
Tasks: Classification, General Classification, Information Retrieval, Retrieval, text-classification, Text Classification

In information retrieval (IR) and related tasks, term weighting approaches
typically consider the frequency of the term in the document and in the
collection in order to compute a score reflecting the importance of the term
for the document. In tasks characterized by the presence of training data (such
as text classification) it seems logical that the term weighting function
should take into account the distribution (as estimated from training data) of
the term across the classes of interest. Although `supervised term weighting'
approaches that use this intuition have been described before, they have failed
to show consistent improvements. In this article we analyse the possible
reasons for this failure, and call consolidated assumptions into question.
Following this criticism we propose a novel supervised term weighting approach
that, instead of relying on any predefined formula, learns a term weighting
function optimised on the training set of interest; we dub this approach
\emph{Learning to Weight} (LTW). The experiments that we run on several
well-known benchmarks, and using different learning methods, show that our
method outperforms previous term weighting approaches in text classification.
GANs-NQM: A Generative Adversarial Networks based No Reference Quality Assessment Metric for RGB-D Synthesized Views
Authors: Suiyi Ling, Jing Li, Junle Wang, Patrick Le Callet
Date: 2019-03-28 00:00:00
Tasks: Local Distortion

In this paper, we proposed a no-reference (NR) quality metric for RGB plus
image-depth (RGB-D) synthesis images based on Generative Adversarial Networks
(GANs), namely GANs-NQM. Due to the failure of the inpainting on dis-occluded
regions in RGB-D synthesis process, to capture the non-uniformly distributed
local distortions and to learn their impact on perceptual quality are
challenging tasks for objective quality metrics. In our study, based on the
characteristics of GANs, we proposed i) a novel training strategy of GANs for
RGB-D synthesis images using existing large-scale computer vision datasets
rather than RGB-D dataset; ii) a referenceless quality metric based on the
trained discriminator by learning a `Bag of Distortion Word' (BDW) codebook and
a local distortion regions selector; iii) a hole filling inpainter, i.e., the
generator of the trained GANs, for RGB-D dis-occluded regions as a side
outcome. According to the experimental results on IRCCyN/IVC DIBR database, the
proposed model outperforms the state-of-the-art quality metrics, in addition,
is more applicable in real scenarios. The corresponding context inpainter also
shows appealing results over other inpainting algorithms.
A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet
Authors: Jean-Marc Valin, Jan Skoglund
Date: 2019-03-28 00:00:00
Tasks: Speech Synthesis

Neural speech synthesis algorithms are a promising new approach for coding speech at very low bitrate. They have so far demonstrated quality that far exceeds traditional vocoders, at the cost of very high complexity. In this work, we present a low-bitrate neural vocoder based on the LPCNet model. The use of linear prediction and sparse recurrent networks makes it possible to achieve real-time operation on general-purpose hardware. We demonstrate that LPCNet operating at 1.6 kb/s achieves significantly higher quality than MELP and that uncompressed LPCNet can exceed the quality of a waveform codec operating at low bitrate. This opens the way for new codec designs based on neural synthesis models.
Robust, fast and accurate: a 3-step method for automatic histological image registration
Authors: Johannes Lotz, Nick Weiss, Stefan Heldmann
Date: 2019-03-28 00:00:00
Tasks: Image Registration

We present a 3-step registration pipeline for differently stained
histological serial sections that consists of 1) a robust pre-alignment, 2) a
parametric registration computed on coarse resolution images, and 3) an
accurate nonlinear registration. In all three steps the NGF distance measure is
minimized with respect to an increasingly flexible transformation. We apply the
method in the ANHIR image registration challenge and evaluate its performance
on the training data. The presented method is robust (error reduction in 99.6%
of the cases), fast (runtime 4 seconds) and accurate (median relative target
registration error 0.19%).
Depth from a polarisation + RGB stereo pair
Authors: Dizhong Zhu, William A. P. Smith
Date: 2019-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry

In this paper, we propose a hybrid depth imaging system in which a
polarisation camera is augmented by a second image from a standard digital
camera. For this modest increase in equipment complexity over conventional
shape-from-polarisation, we obtain a number of benefits that enable us to
overcome longstanding problems with the polarisation shape cue. The stereo cue
provides a depth map which, although coarse, is metrically accurate. This is
used as a guide surface for disambiguation of the polarisation surface normal
estimates using a higher order graphical model. In turn, these are used to
estimate diffuse albedo. By extending a previous shape-from-polarisation method
to the perspective case, we show how to compute dense, detailed maps of
absolute depth, while retaining a linear formulation. We show that our hybrid
method is able to recover dense 3D geometry that is superior to
state-of-the-art shape-from-polarisation or two view stereo alone.
Road User Detection in Videos
Authors: Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier, Pierre Gravel
Date: 2019-03-28 00:00:00
Tasks: object-detection, Object Detection, Video Object Detection

Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not.
Convergence rates for optimised adaptive importance samplers
Authors: Ömer Deniz Akyildiz, Joaquín Míguez
Date: 2019-03-28 00:00:00

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which \textit{adapt} themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $\mathcal{O}(1/\sqrt{N})$ convergence rate as standard importance samplers, where $N$ is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed \textit{optimised adaptive importance samplers} (OAIS). These samplers rely on the iterative minimisation of the $\chi^2$-divergence between an exponential-family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $\mathcal{O}(1/\sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $L_2$ error increases by a factor $\sqrt{\rho^\star} > 1$, where $\rho^\star - 1$ is the minimum $\chi^2$-divergence between the target and an exponential-family proposal.
Counting the learnable functions of structured data
Authors: Pietro Rotondo, Marco Cosentino Lagomarsino, Marco Gherardi
Date: 2019-03-28 00:00:00
Tasks: Object Recognition

Cover's function counting theorem is a milestone in the theory of artificial
neural networks. It provides an answer to the fundamental question of
determining how many binary assignments (dichotomies) of $p$ points in $n$
dimensions can be linearly realized. Regrettably, it has proved hard to extend
the same approach to more advanced problems than the classification of points.
In particular, an emerging necessity is to find methods to deal with structured
data, and specifically with non-pointlike patterns. A prominent case is that of
invariant recognition, whereby identification of a stimulus is insensitive to
irrelevant transformations on the inputs (such as rotations or changes in
perspective in an image). An object is therefore represented by an extended
perceptual manifold, consisting of inputs that are classified similarly. Here,
we develop a function counting theory for structured data of this kind, by
extending Cover's combinatorial technique, and we derive analytical expressions
for the average number of dichotomies of generically correlated sets of
patterns. As an application, we obtain a closed formula for the capacity of a
binary classifier trained to distinguish general polytopes of any dimension.
These results may help extend our theoretical understanding of generalization,
feature extraction, and invariant object recognition by neural networks.
Describing like humans: on diversity in image captioning
Authors: Qingzhong Wang, Antoni B. Chan
Date: 2019-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diversity, Image Captioning, Reinforcement Learning

Recently, the state-of-the-art models for image captioning have overtaken human performance based on the most popular metrics, such as BLEU, METEOR, ROUGE, and CIDEr. Does this mean we have solved the task of image captioning? The above metrics only measure the similarity of the generated caption to the human annotations, which reflects its accuracy. However, an image contains many concepts and multiple levels of detail, and thus there is a variety of captions that express different concepts and details that might be interesting for different humans. Therefore only evaluating accuracy is not sufficient for measuring the performance of captioning models --- the diversity of the generated captions should also be considered. In this paper, we proposed a new metric for measuring the diversity of image captions, which is derived from latent semantic analysis and kernelized to use CIDEr similarity. We conduct extensive experiments to re-evaluate recent captioning models in the context of both diversity and accuracy. We find that there is still a large gap between the model and human performance in terms of both accuracy and diversity and the models that have optimized accuracy (CIDEr) have low diversity. We also show that balancing the cross-entropy loss and CIDEr reward in reinforcement learning during training can effectively control the tradeoff between diversity and accuracy of the generated captions.
Multimodal Deep Network Embedding with Integrated Structure and Attribute Information
Authors: Conghui Zheng, Li Pan, Peng Wu
Date: 2019-03-28 00:00:00
Tasks: Attribute, Network Embedding

Network embedding is the process of learning low-dimensional representations
for nodes in a network, while preserving node features. Existing studies only
leverage network structure information and focus on preserving structural
features. However, nodes in real-world networks often have a rich set of
attributes providing extra semantic information. It has been demonstrated that
both structural and attribute features are important for network analysis
tasks. To preserve both features, we investigate the problem of integrating
structure and attribute information to perform network embedding and propose a
Multimodal Deep Network Embedding (MDNE) method. MDNE captures the non-linear
network structures and the complex interactions among structures and
attributes, using a deep model consisting of multiple layers of non-linear
functions. Since structures and attributes are two different types of
information, a multimodal learning method is adopted to pre-process them and
help the model to better capture the correlations between node structure and
attribute information. We employ both structural proximity and attribute
proximity in the loss function to preserve the respective features and the
representations are obtained by minimizing the loss function. Results of
extensive experiments on four real-world datasets show that the proposed method
performs significantly better than baselines on a variety of tasks, which
demonstrate the effectiveness and generality of our method.