pwc-archive/papers-with-abstracts huggingface dataset:
Expert System for Diagnosis of Chest Diseases Using Neural Networks
Authors: Ismail Kayali
Date: 2018-02-19 00:00:00

This article represents one of the contemporary trends in the application of
the latest methods of information and communication technology for medicine
through an expert system helps the doctor to diagnose some chest diseases which
is important because of the frequent spread of chest diseases nowadays in
addition to the overlap symptoms of these diseases, which is difficult to right
diagnose by doctors with several algorithms: Forward Chaining, Backward
Chaining, Neural Network(Back Propagation). However, this system cannot replace
the doctor function, but it can help the doctor to avoid wrong diagnosis and
treatments. It can also be developed in such a way to help the novice doctors.
Global Pose Estimation with an Attention-based Recurrent Network
Authors: Emilio Parisotto, Devendra Singh Chaplot, Jian Zhang, Ruslan Salakhutdinov
Date: 2018-02-19 00:00:00
Tasks: Feature Engineering, Pose Estimation, Simultaneous Localization and Mapping, Visual Odometry

The ability for an agent to localize itself within an environment is crucial
for many real-world applications. For unknown environments, Simultaneous
Localization and Mapping (SLAM) enables incremental and concurrent building of
and localizing within a map. We present a new, differentiable architecture,
Neural Graph Optimizer, progressing towards a complete neural network solution
for SLAM by designing a system composed of a local pose estimation model, a
novel pose selection module, and a novel graph optimization process. The entire
architecture is trained in an end-to-end fashion, enabling the network to
automatically learn domain-specific features relevant to the visual odometry
and avoid the involved process of feature engineering. We demonstrate the
effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom
environment.
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types
Authors: Hady Elsahar, Christophe Gravier, Frederique Laforest
Date: 2018-02-19 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Knowledge Graphs, Question Generation, Question-Generation, Zero-shot Text Search

We present a neural model for question generation from knowledge base triples
in a "Zero-Shot" setup, that is generating questions for triples containing
predicates, subject types or object types that were not seen at training time.
Our model leverages triples occurrences in the natural language corpus in an
encoder-decoder architecture, paired with an original part-of-speech copy
action mechanism to generate questions. Benchmark and human evaluation show
that our model sets a new state-of-the-art for zero-shot QG.
Classifier Risk Estimation under Limited Labeling Resources
Authors: Anurag Kumar, Bhiksha Raj
Date: 2016-07-09 00:00:00

In this paper we propose strategies for estimating performance of a
classifier when labels cannot be obtained for the whole test set. The number of
test instances which can be labeled is very small compared to the whole test
data size. The goal then is to obtain a precise estimate of classifier
performance using as little labeling resource as possible. Specifically, we try
to answer, how to select a subset of the large test set for labeling such that
the performance of a classifier estimated on this subset is as close as
possible to the one on the whole test set. We propose strategies based on
stratified sampling for selecting this subset. We show that these strategies
can reduce the variance in estimation of classifier accuracy by a significant
amount compared to simple random sampling (over 65% in several cases). Hence,
our proposed methods are much more precise compared to random sampling for
accuracy estimation under restricted labeling resources. The reduction in
number of samples required (compared to random sampling) to estimate the
classifier accuracy with only 1% error is high as 60% in some cases.
Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems
Authors: Nariman Farsad, Andrea Goldsmith
Date: 2018-02-19 00:00:00

The design and analysis of communication systems typically rely on the
development of mathematical models that describe the underlying communication
channel. However, in some systems, such as molecular communication systems
where chemical signals are used for transfer of information, the underlying
channel models are unknown. In these scenarios, a completely new approach to
design and analysis is required. In this work, we focus on one important aspect
of communication systems, the detection algorithms, and demonstrate that by
using tools from deep learning, it is possible to train detectors that perform
well without any knowledge of the underlying channel models. We propose a
technique we call sliding bidirectional recurrent neural network (SBRNN) for
real-time sequence detection. We evaluate this algorithm using experimental
data that is collected by a chemical communication platform, where the channel
model is unknown and difficult to model analytically. We show that deep
learning algorithms perform significantly better than a detector proposed in
previous works, and the SBRNN outperforms other techniques considered in this
work.
Deep Learning for Joint Source-Channel Coding of Text
Authors: Nariman Farsad, Milind Rao, Andrea Goldsmith
Date: 2018-02-19 00:00:00
Tasks: Decoder, Deep Learning

We consider the problem of joint source and channel coding of structured data
such as natural language over a noisy channel. The typical approach to this
problem in both theory and practice involves performing source coding to first
compress the text and then channel coding to add robustness for the
transmission across the channel. This approach is optimal in terms of
minimizing end-to-end distortion with arbitrarily large block lengths of both
the source and channel codes when transmission is over discrete memoryless
channels. However, the optimality of this approach is no longer ensured for
documents of finite length and limitations on the length of the encoding. We
will show in this scenario that we can achieve lower word error rates by
developing a deep learning based encoder and decoder. While the approach of
separate source and channel coding would minimize bit error rates, our approach
preserves semantic information of sentences by first embedding sentences in a
semantic space where sentences closer in meaning are located closer together,
and then performing joint source and channel coding on these embeddings.
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
Authors: Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola
Date: 2018-02-19 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

Scientific and engineering processes deliver massive high-dimensional data
sets that are generated as non-linear transformations of an initial state and
few process parameters. Mapping such data to a low-dimensional manifold
facilitates better understanding of the underlying processes, and enables their
optimization. In this paper, we first show that off-the-shelf non-linear
spectral dimensionality reduction methods, e.g., Isomap, fail for such data,
primarily due to the presence of strong temporal correlations. Then, we propose
a novel method, Entropy-Isomap, to address the issue. The proposed method is
successfully applied to large data describing a fabrication process of organic
materials. The resulting low-dimensional representation correctly captures
process control variables, allows for low-dimensional visualization of the
material morphology evolution, and provides key insights to improve the
process.
Neural Network Detection of Data Sequences in Communication Systems
Authors: Nariman Farsad, Andrea Goldsmith
Date: 2018-01-31 00:00:00

We consider detection based on deep learning, and show it is possible to
train detectors that perform well without any knowledge of the underlying
channel models. Moreover, when the channel model is known, we demonstrate that
it is possible to train detectors that do not require channel state information
(CSI). In particular, a technique we call a sliding bidirectional recurrent
neural network (SBRNN) is proposed for detection where, after training, the
detector estimates the data in real-time as the signal stream arrives at the
receiver. We evaluate this algorithm, as well as other neural network (NN)
architectures, using the Poisson channel model, which is applicable to both
optical and molecular communication systems. In addition, we also evaluate the
performance of this detection method applied to data sent over a molecular
communication platform, where the channel model is difficult to model
analytically. We show that SBRNN is computationally efficient, and can perform
detection under various channel conditions without knowing the underlying
channel model. We also demonstrate that the bit error rate (BER) performance of
the proposed SBRNN detector is better than that of a Viterbi detector with
imperfect CSI as well as that of other NN detectors that have been previously
proposed. Finally, we show that the SBRNN can perform well in rapidly changing
channels, where the coherence time is on the order of a single symbol duration.
Integrated Tools for Engineering Ontologies
Authors: V. Yu. Velychko, K. S. Malakhov, V. V. Semenkov, A. E. Strizhak
Date: 2018-02-19 00:00:00

The article presents an overview of current specialized ontology engineering
tools, as well as texts' annotation tools based on ontologies. The main
functions and features of these tools, their advantages and disadvantages are
discussed. A systematic comparative analysis of means for engineering
ontologies is presented.
Tools for higher-order network analysis
Authors: Austin R. Benson
Date: 2018-02-19 00:00:00
Tasks: Clustering

Networks are a fundamental model of complex systems throughout the sciences,
and network datasets are typically analyzed through lower-order connectivity
patterns described at the level of individual nodes and edges. However,
higher-order connectivity patterns captured by small subgraphs, also called
network motifs, describe the fundamental structures that control and mediate
the behavior of many complex systems. We develop three tools for network
analysis that use higher-order connectivity patterns to gain new insights into
network datasets: (1) a framework to cluster nodes into modules based on joint
participation in network motifs; (2) a generalization of the clustering
coefficient measurement to investigate higher-order closure patterns; and (3) a
definition of network motifs for temporal networks and fast algorithms for
counting them. Using these tools, we analyze data from biology, ecology,
economics, neuroscience, online social networks, scientific collaborations,
telecommunications, transportation, and the World Wide Web.
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Authors: Adina Williams, Nikita Nangia, Samuel R. Bowman
Date: 2017-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: BIG-bench Machine Learning, Domain Adaptation, Natural Language Inference, Sentence

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI)
corpus, a dataset designed for use in the development and evaluation of machine
learning models for sentence understanding. In addition to being one of the
largest corpora available for the task of NLI, at 433k examples, this corpus
improves upon available resources in its coverage: it offers data from ten
distinct genres of written and spoken English--making it possible to evaluate
systems on nearly the full complexity of the language--and it offers an
explicit setting for the evaluation of cross-genre domain adaptation.
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Authors: Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau
Date: 2018-02-19 00:00:00

The rapidly growing body of research in adversarial machine learning has
demonstrated that deep neural networks (DNNs) are highly vulnerable to
adversarially generated images. This underscores the urgent need for practical
defense that can be readily deployed to combat attacks in real-time. Observing
that many attack strategies aim to perturb image pixels in ways that are
visually imperceptible, we place JPEG compression at the core of our proposed
Shield defense framework, utilizing its capability to effectively "compress
away" such pixel manipulation. To immunize a DNN model from artifacts
introduced by compression, Shield "vaccinates" a model by re-training it with
compressed images, where different compression levels are applied to generate
multiple vaccinated models that are ultimately used together in an ensemble
defense. On top of that, Shield adds an additional layer of protection by
employing randomization at test time that compresses different regions of an
image using random compression levels, making it harder for an adversary to
estimate the transformation performed. This novel combination of vaccination,
ensembling, and randomization makes Shield a fortified multi-pronged
protection. We conducted extensive, large-scale experiments using the ImageNet
dataset, and show that our approaches eliminate up to 94% of black-box attacks
and 98% of gray-box attacks delivered by the recent, strongest attacks, such as
Carlini-Wagner's L2 and DeepFool. Our approaches are fast and work without
requiring knowledge about the model.
On the Complexity of Opinions and Online Discussions
Authors: Utkarsh Upadhyay, Abir De, Aasish Pappu, Manuel Gomez-Rodriguez
Date: 2018-02-19 00:00:00

In an increasingly polarized world, demagogues who reduce complexity down to
simple arguments based on emotion are gaining in popularity. Are opinions and
online discussions falling into demagoguery? In this work, we aim to provide
computational tools to investigate this question and, by doing so, explore the
nature and complexity of online discussions and their space of opinions,
uncovering where each participant lies.
  More specifically, we present a modeling framework to construct latent
representations of opinions in online discussions which are consistent with
human judgements, as measured by online voting. If two opinions are close in
the resulting latent space of opinions, it is because humans think they are
similar. Our modeling framework is theoretically grounded and establishes a
surprising connection between opinions and voting models and the sign-rank of a
matrix. Moreover, it also provides a set of practical algorithms to both
estimate the dimension of the latent space of opinions and infer where opinions
expressed by the participants of an online discussion lie in this space.
Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports,
and the Newsroom app suggest that unidimensional opinion models may often be
unable to accurately represent online discussions, provide insights into human
judgements and opinions, and show that our framework is able to circumvent
language nuances such as sarcasm or humor by relying on human judgements
instead of textual analysis.
Divide, Denoise, and Defend against Adversarial Attacks
Authors: Seyed-Mohsen Moosavi-Dezfooli, Ashish Shrivastava, Oncel Tuzel
Date: 2018-02-19 00:00:00
Tasks: Denoising

Deep neural networks, although shown to be a successful class of machine
learning algorithms, are known to be extremely unstable to adversarial
perturbations. Improving the robustness of neural networks against these
attacks is important, especially for security-critical applications. To defend
against such attacks, we propose dividing the input image into multiple
patches, denoising each patch independently, and reconstructing the image,
without losing significant image content. We call our method D3. This proposed
defense mechanism is non-differentiable which makes it non-trivial for an
adversary to apply gradient-based attacks. Moreover, we do not fine-tune the
network with adversarial examples, making it more robust against unknown
attacks. We present an analysis of the tradeoff between accuracy and robustness
against adversarial attacks. We evaluate our method under black-box, grey-box,
and white-box settings. On the ImageNet dataset, our method outperforms the
state-of-the-art by 19.7% under grey-box setting, and performs comparably under
black-box setting. For the white-box setting, the proposed method achieves
34.4% accuracy compared to the 0% reported in the recent works.
As you like it: Localization via paired comparisons
Authors: Andrew K. Massimino, Mark A. Davenport
Date: 2018-02-19 00:00:00

Suppose that we wish to estimate a vector $\mathbf{x}$ from a set of binary paired comparisons of the form "$\mathbf{x}$ is closer to $\mathbf{p}$ than to $\mathbf{q}$" for various choices of vectors $\mathbf{p}$ and $\mathbf{q}$. The problem of estimating $\mathbf{x}$ from this type of observation arises in a variety of contexts, including nonmetric multidimensional scaling, "unfolding," and ranking problems, often because it provides a powerful and flexible model of preference. We describe theoretical bounds for how well we can expect to estimate $\mathbf{x}$ under a randomized model for $\mathbf{p}$ and $\mathbf{q}$. We also present results for the case where the comparisons are noisy and subject to some degree of error. Additionally, we show that under a randomized model for $\mathbf{p}$ and $\mathbf{q}$, a suitable number of binary paired comparisons yield a stable embedding of the space of target vectors. Finally, we also show that we can achieve significant gains by adaptively changing the distribution for choosing $\mathbf{p}$ and $\mathbf{q}$.
Learning Independent Causal Mechanisms
Authors: Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf
Date: 2017-12-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Transfer Learning

Statistical learning relies upon data sampled from a distribution, and we
usually do not care what actually generated it in the first place. From the
point of view of causal modeling, the structure of each distribution is induced
by physical mechanisms that give rise to dependences between observables.
Mechanisms, however, can be meaningful autonomous modules of generative models
that make sense beyond a particular entailed data distribution, lending
themselves to transfer between problems. We develop an algorithm to recover a
set of independent (inverse) mechanisms from a set of transformed data points.
The approach is unsupervised and based on a set of experts that compete for
data generated by the mechanisms, driving specialization. We analyze the
proposed method in a series of experiments on image data. Each expert learns to
map a subset of the transformed data back to a reference distribution. The
learned mechanisms generalize to novel domains. We discuss implications for
transfer learning and links to recent trends in generative modeling.
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Authors: Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
Date: 2017-07-20 00:00:00
Proceeding: NeurIPS 2018 12

We present a unified framework to analyze the global convergence of Langevin dynamics based algorithms for nonconvex finite-sum optimization with $n$ component functions. At the core of our analysis is a direct analysis of the ergodicity of the numerical approximations to Langevin dynamics, which leads to faster convergence rates. Specifically, we show that gradient Langevin dynamics (GLD) and stochastic gradient Langevin dynamics (SGLD) converge to the almost minimizer within $\tilde O\big(nd/(\lambda\epsilon) \big)$ and $\tilde O\big(d^7/(\lambda^5\epsilon^5) \big)$ stochastic gradient evaluations respectively, where $d$ is the problem dimension, and $\lambda$ is the spectral gap of the Markov chain generated by GLD. Both results improve upon the best known gradient complexity results (Raginsky et al., 2017). Furthermore, for the first time we prove the global convergence guarantee for variance reduced stochastic gradient Langevin dynamics (SVRG-LD) to the almost minimizer within $\tilde O\big(\sqrt{n}d^5/(\lambda^4\epsilon^{5/2})\big)$ stochastic gradient evaluations, which outperforms the gradient complexities of GLD and SGLD in a wide regime. Our theoretical analyses shed some light on using Langevin dynamics based algorithms for nonconvex optimization with provable guarantees.
Differentially Private Generative Adversarial Network
Authors: Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou
Date: 2018-02-19 00:00:00
Tasks: Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants have recently attracted
intensive research interests due to their elegant theoretical foundation and
excellent empirical performance as generative models. These tools provide a
promising direction in the studies where data availability is limited. One
common issue in GANs is that the density of the learned generative distribution
could concentrate on the training data points, meaning that they can easily
remember training samples due to the high model complexity of deep networks.
This becomes a major concern when GANs are applied to private or sensitive data
such as patient medical records, and the concentration of distribution may
divulge critical patient information. To address this issue, in this paper we
propose a differentially private GAN (DPGAN) model, in which we achieve
differential privacy in GANs by adding carefully designed noise to gradients
during the learning procedure. We provide rigorous proof for the privacy
guarantee, as well as comprehensive empirical evidence to support our analysis,
where we demonstrate that our method can generate high quality data points at a
reasonable privacy level.
Deep Echo State Networks for Diagnosis of Parkinson's Disease
Authors: Claudio Gallicchio, Alessio Micheli, Luca Pedrelli
Date: 2018-02-19 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we introduce a novel approach for diagnosis of Parkinson's
Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD
is performed by analyzing the whole time-series collected from a tablet device
during the sketching of spiral tests, without the need for feature extraction
and data preprocessing. We evaluated the proposed approach on a public dataset
of spiral tests. The results of experimental analysis show that DeepESNs
perform significantly better than shallow ESN model. Overall, the proposed
approach obtains state-of-the-art results in the identification of PD on this
kind of temporal data.
Analysis of cause-effect inference by comparing regression errors
Authors: Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf
Date: 2018-02-19 00:00:00
Tasks: Causal Inference, regression

We address the problem of inferring the causal direction between two
variables by comparing the least-squares errors of the predictions in both
possible directions. Under the assumption of an independence between the
function relating cause and effect, the conditional noise distribution, and the
distribution of the cause, we show that the errors are smaller in causal
direction if both variables are equally scaled and the causal relation is close
to deterministic. Based on this, we provide an easily applicable algorithm that
only requires a regression in both possible causal directions and a comparison
of the errors. The performance of the algorithm is compared with various
related causal inference methods in different artificial and real-world data
sets.