pwc-archive/papers-with-abstracts huggingface dataset:
The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
Authors: David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst
Date: 2012-10-31 00:00:00
Tasks: Translation

In applications such as social, energy, transportation, sensor, and neuronal
networks, high-dimensional data naturally reside on the vertices of weighted
graphs. The emerging field of signal processing on graphs merges algebraic and
spectral graph theoretic concepts with computational harmonic analysis to
process such signals on graphs. In this tutorial overview, we outline the main
challenges of the area, discuss different ways to define graph spectral
domains, which are the analogues to the classical frequency domain, and
highlight the importance of incorporating the irregular structures of graph
data domains when processing signals on graphs. We then review methods to
generalize fundamental operations such as filtering, translation, modulation,
dilation, and downsampling to the graph setting, and survey the localized,
multiscale transforms that have been proposed to efficiently extract
information from high-dimensional data on graphs. We conclude with a brief
discussion of open issues and possible extensions.
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling
Authors: Jean-Michel Renders
Date: 2017-12-27 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

We consider the problem of Active Search, where a maximum of relevant objects
- ideally all relevant objects - should be retrieved with the minimum effort or
minimum time. Typically, there are two main challenges to face when tackling
this problem: first, the class of relevant objects has often low prevalence
and, secondly, this class can be multi-faceted or multi-modal: objects could be
relevant for completely different reasons. To solve this problem and its
associated issues, we propose an approach based on a non-stationary (aka
restless) extension of Thompson Sampling, a well-known strategy for Multi-Armed
Bandits problems. The collection is first soft-clustered into a finite set of
components and a posterior distribution of getting a relevant object inside
each cluster is updated after receiving the user feedback about the proposed
instances. The "next instance" selection strategy is a mixed, two-level
decision process, where both the soft clusters and their instances are
considered. This method can be considered as an insurance, where the cost of
the insurance is an extra exploration effort in the short run, for achieving a
nearly "total" recall with less efforts in the long run.
Higher-order Graph Convolutional Networks
Authors: John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao
Date: 2018-09-12 00:00:00
Tasks: General Classification, Graph Attention, Node Classification

Following the success of deep convolutional networks in various vision and
speech related tasks, researchers have started investigating generalizations of
the well-known technique for graph-structured data. A recently-proposed method
called Graph Convolutional Networks has been able to achieve state-of-the-art
results in the task of node classification. However, since the proposed method
relies on localized first-order approximations of spectral graph convolutions,
it is unable to capture higher-order interactions between nodes in the graph.
In this work, we propose a motif-based graph attention model, called Motif
Convolutional Networks (MCNs), which generalizes past approaches by using
weighted multi-hop motif adjacency matrices to capture higher-order
neighborhoods. A novel attention mechanism is used to allow each individual
node to select the most relevant neighborhood to apply its filter. Experiments
show that our proposed method is able to achieve state-of-the-art results on
the semi-supervised node classification task.
MedSTS: A Resource for Clinical Semantic Textual Similarity
Authors: Yanshan Wang, Naveed Afzal, Sunyang Fu, Li-Wei Wang, Feichen Shen, Majid Rastegar-Mojarad, Hongfang Liu
Date: 2018-08-28 00:00:00
Tasks: Decision Making, Semantic Similarity, Semantic Textual Similarity, Sentence, STS

The wide adoption of electronic health records (EHRs) has enabled a wide
range of applications leveraging EHR data. However, the meaningful use of EHR
data largely depends on our ability to efficiently extract and consolidate
information embedded in clinical text where natural language processing (NLP)
techniques are essential. Semantic textual similarity (STS) that measures the
semantic similarity between text snippets plays a significant role in many NLP
applications. In the general NLP domain, STS shared tasks have made available a
huge collection of text snippet pairs with manual annotations in various
domains. In the clinical domain, STS can enable us to detect and eliminate
redundant information that may lead to a reduction in cognitive burden and an
improvement in the clinical decision-making process. This paper elaborates our
efforts to assemble a resource for STS in the medical domain, MedSTS. It
consists of a total of 174,629 sentence pairs gathered from a clinical corpus
at Mayo Clinic. A subset of MedSTS (MedSTS_ann) containing 1,068 sentence pairs
was annotated by two medical experts with semantic similarity scores of 0-5
(low to high similarity). We further analyzed the medical concepts in the
MedSTS corpus, and tested four STS systems on the MedSTS_ann corpus. In the
future, we will organize a shared task by releasing the MedSTS_ann corpus to
motivate the community to tackle the real world clinical problems.
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Authors: Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian
Date: 2018-10-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multi-Task Learning, Prognosis, Transfer Learning

Precision medicine aims for personalized prognosis and therapeutics by
utilizing recent genome-scale high-throughput profiling techniques, including
next-generation sequencing (NGS). However, translating NGS data faces several
challenges. First, NGS count data are often overdispersed, requiring
appropriate modeling. Second, compared to the number of involved molecules and
system complexity, the number of available samples for studying complex
disease, such as cancer, is often limited, especially considering disease
heterogeneity. The key question is whether we may integrate available data from
all different sources or domains to achieve reproducible disease prognosis
based on NGS count data. In this paper, we develop a Bayesian Multi-Domain
Learning (BMDL) model that derives domain-dependent latent representations of
overdispersed count data based on hierarchical negative binomial factorization
for accurate cancer subtyping even if the number of samples for a specific
cancer type is small. Experimental results from both our simulated and NGS
datasets from The Cancer Genome Atlas (TCGA) demonstrate the promising
potential of BMDL for effective multi-domain learning without "negative
transfer" effects often seen in existing multi-task learning and transfer
learning methods.
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition
Authors: Christopher Dane Shulby, Leonardo Pombal, Vitor Jordão, Guilherme Ziolle, Bruno Martho, Antônio Postal, Thiago Prochnow
Date: 2018-10-22 00:00:00
Tasks: Data Augmentation, speech-recognition, Speech Recognition, Word Embeddings

Violence is an epidemic in Brazil and a problem on the rise world-wide.
Mobile devices provide communication technologies which can be used to monitor
and alert about violent situations. However, current solutions, like panic
buttons or safe words, might increase the loss of life in violent situations.
We propose an embedded artificial intelligence solution, using natural language
and speech processing technology, to silently alert someone who can help in
this situation. The corpus used contains 400 positive phrases and 800 negative
phrases, totaling 1,200 sentences which are classified using two well-known
extraction methods for natural language processing tasks: bag-of-words and word
embeddings and classified with a support vector machine. We describe the
proof-of-concept product in development with promising results, indicating a
path towards a commercial product. More importantly we show that model
improvements via word embeddings and data augmentation techniques provide an
intrinsically robust model. The final embedded solution also has a small
footprint of less than 10 MB.
An Exploration of Dropout with RNNs for Natural Language Inference
Authors: Amit Gajbhiye, Sardar Jaf, Noura Al Moubayed, A. Stephen McGough, Steven Bradley
Date: 2018-10-22 00:00:00
Tasks: Natural Language Inference

Dropout is a crucial regularization technique for the Recurrent Neural
Network (RNN) models of Natural Language Inference (NLI). However, dropout has
not been evaluated for the effectiveness at different layers and dropout rates
in NLI models. In this paper, we propose a novel RNN model for NLI and
empirically evaluate the effect of applying dropout at different layers in the
model. We also investigate the impact of varying dropout rates at these layers.
Our empirical evaluation on a large (Stanford Natural Language Inference
(SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward
connection severely affects the model accuracy at increasing dropout rates. We
also show that regularizing the embedding layer is efficient for SNLI whereas
regularizing the recurrent layer improves the accuracy for SciTail. Our model
achieved an accuracy 86.14% on the SNLI dataset and 77.05% on SciTail.
Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting
Authors: Philipp Haehnel, Jakub Marecek, Julien Monteil, Fearghal O'Donncha
Date: 2018-10-22 00:00:00
Tasks: Deep Learning

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-learning techniques were limited to domains, where the PDE solver was applicable. We demonstrate a deep-learning framework for air-pollution monitoring and forecasting that provides the ability to train across different model domains, as well as a reduction in the run-time by two orders of magnitude. It presents a first-of-a-kind implementation that combines deep-learning and domain-decomposition techniques to allow model deployments extend beyond the domain(s) on which the it has been trained.
Optimality of the final model found via Stochastic Gradient Descent
Authors: Andrea Schioppa
Date: 2018-10-22 00:00:00

We study convergence properties of Stochastic Gradient Descent (SGD) for
convex objectives without assumptions on smoothness or strict convexity. We
consider the question of establishing that with high probability the objective
evaluated at the candidate minimizer returned by SGD is close to the minimal
value of the objective. We compare this result concerning the final candidate
minimzer (i.e. the final model parameters learned after all gradient steps) to
the online learning techniques of [Zin03] that take a rolling average of the
model parameters at the different steps of SGD.
Properties of an N Time-Slice Dynamic Chain Event Graph
Authors: Rodrigo A. Collazo, Jim Q. Smith
Date: 2018-10-22 00:00:00

A Dynamic Chain Event Graph (DCEG) provides a rich tree-based framework for
modelling a dynamic process with highly asymmetric developments. An N
Time-Slice DCEG (NT-DCEG) is a useful subclass of the DCEG class that exhibits
a specific type of periodicity in its supporting tree graph and embodies a
time-homogeneity assumption. Here some desired properties of an NT-DCEG is
explored. In particular, we prove that the class of NT-DCEGs contains all
discrete N time-slice Dynamic Bayesian Networks as special cases. We also
develop a method to distributively construct an NT-DCEG model. By exploiting
the topology of an NT-DCEG graph, we show how to construct intrinsic random
variables which exhibit context-specific independences that can then be checked
by domain experts. We also show how an NT-DCEG can be used to depict various
structural and Granger causal hypotheses about a given process. Our methods are
illustrated throughout using examples of dynamic multivariate processes
describing inmate radicalisation in a prison.
Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge
Authors: Matthias Meyer, Timo Farei-Campagna, Akos Pasztor, Reto Da Forno, Tonio Gsell, Jérome Faillettaz, Andreas Vieli, Samuel Weber, Jan Beutel, Lothar Thiele
Date: 2018-10-22 00:00:00
Tasks: Decision Making

In natural hazard warning systems fast decision making is vital to avoid
catastrophes. Decision making at the edge of a wireless sensor network promises
fast response times but is limited by the availability of energy, data transfer
speed, processing and memory constraints. In this work we present a realization
of a wireless sensor network for hazard monitoring based on an array of
event-triggered single-channel micro-seismic sensors with advanced signal
processing and characterization capabilities based on a novel co-detection
technique. On the one hand we leverage an ultra-low power, threshold-triggering
circuit paired with on-demand digital signal acquisition capable of extracting
relevant information exactly and efficiently at times when it matters most and
consequentially not wasting precious resources when nothing can be observed. On
the other hand we utilize machine-learning-based classification implemented on
low-power, off-the-shelf microcontrollers to avoid false positive warnings and
to actively identify humans in hazard zones. The sensors' response time and
memory requirement is substantially improved by quantizing and pipelining the
inference of a convolutional neural network. In this way, convolutional neural
networks that would not run unmodified on a memory constrained device can be
executed in real-time and at scale on low-power embedded devices. A field study
with our system is running on the rockfall scarp of the Matterhorn H\"ornligrat
at 3500 m a.s.l. since 08/2018.
Alternating Linear Bandits for Online Matrix-Factorization Recommendation
Authors: Hamid Dadkhahi, Sahand Negahban
Date: 2018-10-22 00:00:00
Tasks: Collaborative Filtering

We consider the problem of online collaborative filtering in the online
setting, where items are recommended to the users over time. At each time step,
the user (selected by the environment) consumes an item (selected by the agent)
and provides a rating of the selected item. In this paper, we propose a novel
algorithm for online matrix factorization recommendation that combines linear
bandits and alternating least squares. In this formulation, the bandit feedback
is equal to the difference between the ratings of the best and selected items.
We evaluate the performance of the proposed algorithm over time using both
cumulative regret and average cumulative NDCG. Simulation results over three
synthetic datasets as well as three real-world datasets for online
collaborative filtering indicate the superior performance of the proposed
algorithm over two state-of-the-art online algorithms.
A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding
Authors: Constantine Dovrolis
Date: 2018-10-22 00:00:00
Tasks: Clustering, Continual Learning, Online Clustering

We propose that the Continual Learning desiderata can be achieved through a
neuro-inspired architecture, grounded on Mountcastle's cortical column
hypothesis. The proposed architecture involves a single module, called
Self-Taught Associative Memory (STAM), which models the function of a cortical
column. STAMs are repeated in multi-level hierarchies involving feedforward,
lateral and feedback connections. STAM networks learn in an unsupervised
manner, based on a combination of online clustering and hierarchical predictive
coding. This short paper only presents the architecture and its connections
with neuroscience. A mathematical formulation and experimental results will be
presented in an extended version of this paper.
A minimax near-optimal algorithm for adaptive rejection sampling
Authors: Juliette Achdou, Joseph C. Lam, Alexandra Carpentier, Gilles Blanchard
Date: 2018-10-22 00:00:00

Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample
from distributions admitting a probability density function which can be
evaluated exactly at any given point, albeit at a high computational cost.
However, without proper tuning, this technique implies a high rejection rate.
Several methods have been explored to cope with this problem, based on the
principle of adaptively estimating the density by a simpler function, using the
information of the previous samples. Most of them either rely on strong
assumptions on the form of the density, or do not offer any theoretical
performance guarantee. We give the first theoretical lower bound for the
problem of adaptive rejection sampling and introduce a new algorithm which
guarantees a near-optimal rejection rate in a minimax sense.
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
Authors: Eldar Insafutdinov, Alexey Dosovitskiy
Date: 2018-10-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: 3D Pose Estimation

We address the problem of learning accurate 3D shape and camera pose from a
collection of unlabeled category-specific images. We train a convolutional
network to predict both the shape and the pose from a single image by
minimizing the reprojection error: given several views of an object, the
projections of the predicted shapes to the predicted camera poses should match
the provided views. To deal with pose ambiguity, we introduce an ensemble of
pose predictors which we then distill to a single "student" model. To allow for
efficient learning of high-fidelity shapes, we represent the shapes by point
clouds and devise a formulation allowing for differentiable projection of
these. Our experiments show that the distilled ensemble of pose predictors
learns to estimate the pose accurately, while the point cloud representation
allows to predict detailed shape models. The supplementary video can be found
at https://www.youtube.com/watch?v=LuIGovKeo60
Linguistic Legal Concept Extraction in Portuguese
Authors: Alessandra Cid, Alexandre Rademaker, Bruno Cuconato, Valeria de Paiva
Date: 2018-10-22 00:00:00
Tasks: Ethics, Multiple-choice

This work investigates legal concepts and their expression in Portuguese,
concentrating on the "Order of Attorneys of Brazil" Bar exam. Using a corpus
formed by a collection of multiple-choice questions, three norms related to the
Ethics part of the OAB exam, language resources (Princeton WordNet and
OpenWordNet-PT) and tools (AntConc and Freeling), we began to investigate the
concepts and words missing from our repertory of concepts and words in
Portuguese, the knowledge base OpenWordNet-PT. We add these concepts and words
to OpenWordNet-PT and hence obtain a representation of these texts that is
"contained" in the lexical knowledge base.
Predictive Linguistic Features of Schizophrenia
Authors: Efsun Sarioglu Kayi, Mona Diab, Luca Pauselli, Michael Compton, Glen Coppersmith
Date: 2018-10-22 00:00:00
Proceeding: SEMEVAL 2017 8

Schizophrenia is one of the most disabling and difficult to treat of all
human medical/health conditions, ranking in the top ten causes of disability
worldwide. It has been a puzzle in part due to difficulty in identifying its
basic, fundamental components. Several studies have shown that some
manifestations of schizophrenia (e.g., the negative symptoms that include
blunting of speech prosody, as well as the disorganization symptoms that lead
to disordered language) can be understood from the perspective of linguistics.
However, schizophrenia research has not kept pace with technologies in
computational linguistics, especially in semantics and pragmatics. As such, we
examine the writings of schizophrenia patients analyzing their syntax,
semantics and pragmatics. In addition, we analyze tweets of (self pro-claimed)
schizophrenia patients who publicly discuss their diagnoses. For writing
samples dataset, syntactic features are found to be the most successful in
classification whereas for the less structured Twitter dataset, a combination
of features performed the best.
Brain Tumor Image Retrieval via Multitask Learning
Authors: Maxim Pisov, Gleb Makarchuk, Valery Kostjuchenko, Alexandra Dalechina, Andrey Golanov, Mikhail Belyaev
Date: 2018-10-22 00:00:00
Tasks: Classification, General Classification, Image Retrieval, Retrieval

Classification-based image retrieval systems are built by training
convolutional neural networks (CNNs) on a relevant classification problem and
using the distance in the resulting feature space as a similarity metric.
However, in practical applications, it is often desirable to have
representations which take into account several aspects of the data (e.g.,
brain tumor type and its localization). In our work, we extend the
classification-based approach with multitask learning: we train a CNN on brain
MRI scans with heterogeneous labels and implement a corresponding tumor image
retrieval system. We validate our approach on brain tumor data which contains
information about tumor types, shapes and localization. We show that our method
allows us to build representations that contain more relevant information about
tumors than single-task classification-based approaches.
Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning
Authors: Guillaume Devineau, Philip Polack, Florent Altché, Fabien Moutarde
Date: 2018-10-22 00:00:00

This paper explores the capability of deep neural networks to capture key
characteristics of vehicle dynamics, and their ability to perform coupled
longitudinal and lateral control of a vehicle. To this extent, two different
artificial neural networks are trained to compute vehicle controls
corresponding to a reference trajectory, using a dataset based on high-fidelity
simulations of vehicle dynamics. In this study, control inputs are chosen as
the steering angle of the front wheels, and the applied torque on each wheel.
The performance of both models, namely a Multi-Layer Perceptron (MLP) and a
Convolutional Neural Network (CNN), is evaluated based on their ability to
drive the vehicle on a challenging test track, shifting between long straight
lines and tight curves. A comparison to conventional decoupled controllers on
the same track is also provided.
ensmallen: a flexible C++ library for efficient function optimization
Authors: Shikhar Bhardwaj, Ryan R. Curtin, Marcus Edel, Yannis Mentekidis, Conrad Sanderson
Date: 2018-10-22 00:00:00
Tasks: BIG-bench Machine Learning

We present ensmallen, a fast and flexible C++ library for mathematical
optimization of arbitrary user-supplied functions, which can be applied to many
machine learning problems. Several types of optimizations are supported,
including differentiable, separable, constrained, and categorical objective
functions. The library provides many pre-built optimizers (including numerous
variants of SGD and Quasi-Newton optimizers) as well as a flexible framework
for implementing new optimizers and objective functions. Implementation of a
new optimizer requires only one method and a new objective function requires
typically one or two C++ functions. This can aid in the quick implementation
and prototyping of new machine learning algorithms. Due to the use of C++
template metaprogramming, ensmallen is able to support compiler optimizations
that provide fast runtimes. Empirical comparisons show that ensmallen is able
to outperform other optimization frameworks (like Julia and SciPy), sometimes
by large margins. The library is distributed under the BSD license and is ready
for use in production environments.