pwc-archive/papers-with-abstracts huggingface dataset:
Deep-learning the Latent Space of Light Transport
Authors: Pedro Hermosilla, Sebastian Maisch, Tobias Ritschel, Timo Ropinski
Date: 2018-11-12 00:00:00
Tasks: 3D geometry, Computational Efficiency, Deep Learning

We suggest a method to directly deep-learn light transport, i. e., the mapping from a 3D geometry-illumination-material configuration to a shaded 2D image. While many previous learning methods have employed 2D convolutional neural networks applied to images, we show for the first time that light transport can be learned directly in 3D. The benefit of 3D over 2D is, that the former can also correctly capture illumination effects related to occluded and/or semi-transparent geometry. To learn 3D light transport, we represent the 3D scene as an unstructured 3D point cloud, which is later, during rendering, projected to the 2D output image. Thus, we suggest a two-stage operator comprising of a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step. We will show that our approach results in improved quality in terms of temporal coherence while retaining most of the computational efficiency of common 2D methods. As a consequence, the proposed two stage-operator serves as a valuable extension to modern deferred shading approaches.
Learning Representations of Missing Data for Predicting Patient Outcomes
Authors: Brandon Malone, Alberto Garcia-Duran, Mathias Niepert
Date: 2018-11-12 00:00:00
Tasks: Binary Classification, Classification, General Classification, Predicting Patient Outcomes, regression, Representation Learning, Time Series, Time Series Analysis

Extracting actionable insight from Electronic Health Records (EHRs) poses
several challenges for traditional machine learning approaches. Patients are
often missing data relative to each other; the data comes in a variety of
modalities, such as multivariate time series, free text, and categorical
demographic information; important relationships among patients can be
difficult to detect; and many others. In this work, we propose a novel approach
to address these first three challenges using a representation learning scheme
based on message passing. We show that our proposed approach is competitive
with or outperforms the state of the art for predicting in-hospital mortality
(binary classification), the length of hospital visits (regression) and the
discharge destination (multiclass classification).
Gaussian AutoEncoder
Authors: Jarek Duda
Date: 2018-11-12 00:00:00
Tasks: Data Compression, Quantization

Generative AutoEncoders require a chosen probability distribution in latent
space, usually multivariate Gaussian. The original Variational AutoEncoder
(VAE) uses randomness in encoder - causing problematic distortion, and overlaps
in latent space for distinct inputs. It turned out unnecessary: we can instead
use deterministic encoder with additional regularizer to ensure that sample
distribution in latent space is close to the required. The original approach
(WAE) uses Wasserstein metric, what required comparing with random sample and
using an arbitrarily chosen kernel. Later CWAE finally derived a non-random
analytic formula by averaging $L_2$ distance of Gaussian-smoothened sample over
all 1D projections. However, these arbitrarily chosen regularizers do not lead
to Gaussian distribution.
  This article proposes approach for regularizers directly optimizing agreement
between empirical distribution function and its desired CDF for chosen
properties, for example radii and distances for Gaussian distribution, or
coordinate-wise, to directly attract this distribution in latent space of
AutoEncoder. We can also attract different distributions with this general
approach, for example latent space uniform distribution on $[0,1]^D$ hypercube
or torus would allow for data compression without entropy coding, increased
density near codewords would optimize for the required quantization.
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
Authors: Robert Walecki, Albert Buchard, Kostis Gourgoulias, Chris Hart, Maria Lomeli, A. K. W. Navarro, Max Zwiessele, Yura Perov, Saurabh Johri
Date: 2018-11-12 00:00:00
Tasks: Clustering

Probabilistic graphical models are powerful tools which allow us to formalise
our knowledge about the world and reason about its inherent uncertainty. There
exist a considerable number of methods for performing inference in
probabilistic graphical models; however, they can be computationally costly due
to significant time burden and/or storage requirements; or they lack
theoretical guarantees of convergence and accuracy when applied to large scale
graphical models. To this end, we propose the Universal Marginaliser Importance
Sampler (UM-IS) -- a hybrid inference scheme that combines the flexibility of a
deep neural network trained on samples from the model and inherits the
asymptotic guarantees of importance sampling. We show how combining samples
drawn from the graphical model with an appropriate masking function allows us
to train a single neural network to approximate any of the corresponding
conditional marginal distributions, and thus amortise the cost of inference. We
also show that the graph embeddings can be applied for tasks such as:
clustering, classification and interpretation of relationships between the
nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing
that UM-IS outperforms sampling-based methods by a large margin while being
computationally efficient.
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification
Authors: Jindřich Libovický, Jindřich Helcl
Date: 2018-11-12 00:00:00
Tasks: Decoder, General Classification, Machine Translation, Translation

Autoregressive decoding is the only part of sequence-to-sequence models that
prevents them from massive parallelization at inference time.
Non-autoregressive models enable the decoder to generate all output symbols
independently in parallel. We present a novel non-autoregressive architecture
based on connectionist temporal classification and evaluate it on the task of
neural machine translation. Unlike other non-autoregressive methods which
operate in several steps, our model can be trained end-to-end. We conduct
experiments on the WMT English-Romanian and English-German datasets. Our models
achieve a significant speedup over the autoregressive models, keeping the
translation quality comparable to other non-autoregressive models.
Input Combination Strategies for Multi-Source Transformer Decoder
Authors: Jindřich Libovický, Jindřich Helcl, David Mareček
Date: 2018-11-12 00:00:00
Tasks: Decoder, Translation

In multi-source sequence-to-sequence tasks, the attention mechanism can be
modeled in several ways. This topic has been thoroughly studied on recurrent
architectures. In this paper, we extend the previous work to the
encoder-decoder attention in the Transformer architecture. We propose four
different input combination strategies for the encoder-decoder attention:
serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of
multimodal translation and translation with multiple source languages. The
experiments show that the models are able to use multiple sources and improve
over single source baselines.
Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
Authors: Michael Chertkov, Vladimir Chernyak, Yury Maximov
Date: 2018-11-12 00:00:00

Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is of an exponential complexity with respect to the number of variables. In this manuscript, aimed at approximating the PF, we consider Multi-Graph Models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge Function technique with the technique from the field of real stable polynomials. We show that the Gauge Function has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, it can be used to recover the Partition Function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists in application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination of edges resulting in a sequence of models on decreasing in size graphs with the same Partition Function. Even though complexity of computing factors in the sequence models grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that Belief Propagation estimations in the sequence do not decrease, each low-bounding the Partition Function.
Analyzing deep CNN-based utterance embeddings for acoustic model adaptation
Authors: Joanna Rownicka, Peter Bell, Steve Renals
Date: 2018-11-12 00:00:00
Tasks: speech-recognition, Speech Recognition

We explore why deep convolutional neural networks (CNNs) with small
two-dimensional kernels, primarily used for modeling spatial relations in
images, are also effective in speech recognition. We analyze the
representations learned by deep CNNs and compare them with deep neural network
(DNN) representations and i-vectors, in the context of acoustic model
adaptation. To explore whether interpretable information can be decoded from
the learned representations we evaluate their ability to discriminate between
speakers, acoustic conditions, noise type, and gender using the Aurora-4
dataset. We extract both whole model embeddings (to capture the information
learned across the whole network) and layer-specific embeddings which enable
understanding of the flow of information across the network. We also use
learned representations as the additional input for a time-delay neural network
(TDNN) for the Aurora-4 and MGB-3 English datasets. We find that deep CNN
embeddings outperform DNN embeddings for acoustic model adaptation and
auxiliary features based on deep CNN embeddings result in similar word error
rates to i-vectors.
CUNI System for the WMT18 Multimodal Translation Task
Authors: Jindřich Helcl, Jindřich Libovický, Dušan Variš
Date: 2018-11-12 00:00:00
Tasks: Translation

We present our submission to the WMT18 Multimodal Translation Task. The main
feature of our submission is applying a self-attentive network instead of a
recurrent neural network. We evaluate two methods of incorporating the visual
features in the model: first, we include the image representation as another
input to the network; second, we train the model to predict the visual features
and use it as an auxiliary objective. For our submission, we acquired both
textual and multimodal additional data. Both of the proposed methods yield
significant improvements over recurrent networks and self-attentive textual
baselines.
Classifying Patent Applications with Ensemble Methods
Authors: Fernando Benites, Shervin Malmasi, Marcos Zampieri
Date: 2018-11-12 00:00:00
Proceeding: ALTA 2018 12
Tasks: Classification, General Classification, Patent classification

We present methods for the automatic classification of patent applications
using an annotated dataset provided by the organizers of the ALTA 2018 shared
task - Classifying Patent Applications. The goal of the task is to use
computational methods to categorize patent applications according to a
coarse-grained taxonomy of eight classes based on the International Patent
Classification (IPC). We tested a variety of approaches for this task and the
best results, 0.778 micro-averaged F1-Score, were achieved by SVM ensembles
using a combination of words and characters as features. Our team, BMZ, was
ranked first among 14 teams in the competition.
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
Authors: Che-Ping Tsai, Hung-Yi Lee
Date: 2018-11-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Recent work has shown that exploiting relations between labels improves the
performance of multi-label classification. We propose a novel framework based
on generative adversarial networks (GANs) to model label dependency. The
discriminator learns to model label dependency by discriminating real and
generated label sets. To fool the discriminator, the classifier, or generator,
learns to generate label sets with dependencies close to real data. Extensive
experiments and comparisons on two large-scale image classification benchmark
datasets (MS-COCO and NUS-WIDE) show that the discriminator improves
generalization ability for different kinds of models
Learning Segmentation Masks with the Independence Prior
Authors: Songmin Dai, Xiaoqiang Li, Lu Wang, Pin Wu, Weiqin Tong, Yimin Chen
Date: 2018-11-12 00:00:00
Tasks: Foreground Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

An instance with a bad mask might make a composite image that uses it look
fake. This encourages us to learn segmentation by generating realistic
composite images. To achieve this, we propose a novel framework that exploits a
new proposed prior called the independence prior based on Generative
Adversarial Networks (GANs). The generator produces an image with multiple
category-specific instance providers, a layout module and a composition module.
Firstly, each provider independently outputs a category-specific instance image
with a soft mask. Then the provided instances' poses are corrected by the
layout module. Lastly, the composition module combines these instances into a
final image. Training with adversarial loss and penalty for mask area, each
provider learns a mask that is as small as possible but enough to cover a
complete category-specific instance. Weakly supervised semantic segmentation
methods widely use grouping cues modeling the association between image parts,
which are either artificially designed or learned with costly segmentation
labels or only modeled on local pairs. Unlike them, our method automatically
models the dependence between any parts and learns instance segmentation. We
apply our framework in two cases: (1) Foreground segmentation on
category-specific images with box-level annotation. (2) Unsupervised learning
of instance appearances and masks with only one image of homogeneous object
cluster (HOC). We get appealing results in both tasks, which shows the
independence prior is useful for instance segmentation and it is possible to
unsupervisedly learn instance masks with only one image.
Towards Adversarial Denoising of Radar Micro-Doppler Signatures
Authors: Sherif Abdulatif, Karim Armanious, Fady Aziz, Urs Schneider, Bin Yang
Date: 2018-11-12 00:00:00
Tasks: Denoising, Denoising Of Radar Micro-Doppler Signatures, Image Generation

Generative Adversarial Networks (GANs) are considered the state-of-the-art in the field of image generation. They learn the joint distribution of the training data and attempt to generate new data samples in high dimensional space following the same distribution as the input. Recent improvements in GANs opened the field to many other computer vision applications based on improving and changing the characteristics of the input image to follow some given training requirements. In this paper, we propose a novel technique for the denoising and reconstruction of the micro-Doppler ($\boldsymbol{\mu}$-D) spectra of walking humans based on GANs. Two sets of experiments were collected on 22 subjects walking on a treadmill at an intermediate velocity using a \unit[25]{GHz} CW radar. In one set, a clean $\boldsymbol{\mu}$-D spectrum is collected for each subject by placing the radar at a close distance to the subject. In the other set, variations are introduced in the experiment setup to introduce different noise and clutter effects on the spectrum by changing the distance and placing reflective objects between the radar and the target. Synthetic paired noisy and noise-free spectra were used for training, while validation was carried out on the real noisy measured data. Finally, qualitative and quantitative comparison with other classical radar denoising approaches in the literature demonstrated the proposed GANs framework is better and more robust to different noise levels.
A Deep Ensemble Framework for Fake News Detection and Classification
Authors: Arjun Roy, Kingshuk Basak, Asif Ekbal, Pushpak Bhattacharyya
Date: 2018-11-12 00:00:00
Tasks: Classification, Fake News Detection, General Classification, Misinformation

Fake news, rumor, incorrect information, and misinformation detection are
nowadays crucial issues as these might have serious consequences for our social
fabrics. The rate of such information is increasing rapidly due to the
availability of enormous web information sources including social media feeds,
news blogs, online newspapers etc.
  In this paper, we develop various deep learning models for detecting fake
news and classifying them into the pre-defined fine-grained categories.
  At first, we develop models based on Convolutional Neural Network (CNN) and
Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations
obtained from these two models are fed into a Multi-layer Perceptron Model
(MLP) for the final classification. Our experiments on a benchmark dataset show
promising results with an overall accuracy of 44.87\%, which outperforms the
current state of the art.
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
Authors: Navin Cooray, Fernando Andreotti, Christine Lo, Mkael Symmonds, Michele T. M. Hu, Maarten De Vos
Date: 2018-11-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Staging

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is
an early predictor of Parkinson's disease. This study proposes a
fully-automated framework for RBD detection consisting of automated sleep
staging followed by RBD identification. Analysis was assessed using a limited
polysomnography montage from 53 participants with RBD and 53 age-matched
healthy controls. Sleep stage classification was achieved using a Random Forest
(RF) classifier and 156 features extracted from electroencephalogram (EEG),
electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a
RF classifier was trained combining established techniques to quantify muscle
atonia with additional features that incorporate sleep architecture and the EMG
fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's
Kappa score. RBD detection accuracy improved by 10% to 96% (compared to
individual established metrics) when using manually annotated sleep staging.
Accuracy remained high (92%) when using automated sleep staging. This study
outperforms established metrics and demonstrates that incorporating sleep
architecture and sleep stage transitions can benefit RBD detection. This study
also achieved automated sleep staging with a level of accuracy comparable to
manual annotation. This study validates a tractable, fully-automated, and
sensitive pipeline for RBD identification that could be translated to wearable
take-home technology.
RelDenClu: A Relative Density based Biclustering Method for identifying non-linear feature relations
Authors: Namita Jain, Susmita Ghosh, C. A. Murthy
Date: 2018-11-12 00:00:00

The existing biclustering algorithms for finding feature relation based biclusters often depend on assumptions like monotonicity or linearity. Though a few algorithms overcome this problem by using density-based methods, they tend to miss out many biclusters because they use global criteria for identifying dense regions. The proposed method, RelDenClu uses the local variations in marginal and joint densities for each pair of features to find the subset of observations, which forms the bases of the relation between them. It then finds the set of features connected by a common set of observations, resulting in a bicluster. To show the effectiveness of the proposed methodology, experimentation has been carried out on fifteen types of simulated datasets. Further, it has been applied to six real-life datasets. For three of these real-life datasets, the proposed method is used for unsupervised learning, while for other three real-life datasets it is used as an aid to supervised learning. For all the datasets the performance of the proposed method is compared with that of seven different state-of-the-art algorithms and the proposed algorithm is seen to produce better results. The efficacy of proposed algorithm is also seen by its use on COVID-19 dataset for identifying some features (genetic, demographics and others) that are likely to affect the spread of COVID-19.
Not Just Depressed: Bipolar Disorder Prediction on Reddit
Authors: Ivan Sekulić, Matej Gjurković, Jan Šnajder
Date: 2018-11-12 00:00:00
Tasks: Prediction

Bipolar disorder, an illness characterized by manic and depressive episodes,
affects more than 60 million people worldwide. We present a preliminary study
on bipolar disorder prediction from user-generated text on Reddit, which relies
on users' self-reported labels. Our benchmark classifiers for bipolar disorder
prediction outperform the baselines and reach accuracy and F1-scores of above
86%. Feature analysis shows interesting differences in language use between
users with bipolar disorders and the control group, including differences in
the use of emotion-expressive words.
Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation
Authors: Pablo Samuel Castro, Maria Attarian
Date: 2018-11-12 00:00:00
Tasks: Diversity

The use of language models for generating lyrics and poetry has received an
increased interest in the last few years. They pose a unique challenge relative
to standard natural language problems, as their ultimate purpose is reative,
notions of accuracy and reproducibility are secondary to notions of lyricism,
structure, and diversity. In this creative setting, traditional quantitative
measures for natural language problems, such as BLEU scores, prove inadequate:
a high-scoring model may either fail to produce output respecting the desired
structure (e.g. song verses), be a terribly boring creative companion, or both.
In this work we propose a mechanism for combining two separately trained
language models into a framework that is able to produce output respecting the
desired song structure, while providing a richness and diversity of vocabulary
that renders it more creatively appealing.