pwc-archive/papers-with-abstracts huggingface dataset:
Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning
Authors: Artur Filipowicz, Thee Chanyaswad, S. Y. Kung
Date: 2017-07-24 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

The quest for better data analysis and artificial intelligence has lead to
more and more data being collected and stored. As a consequence, more data are
exposed to malicious entities. This paper examines the problem of privacy in
machine learning for classification. We utilize the Ridge Discriminant
Component Analysis (RDCA) to desensitize data with respect to a privacy label.
Based on five experiments, we show that desensitization by RDCA can effectively
protect privacy (i.e. low accuracy on the privacy label) with small loss in
utility. On HAR and CMU Faces datasets, the use of desensitized data results in
random guess level accuracies for privacy at a cost of 5.14% and 0.04%, on
average, drop in the utility accuracies. For Semeion Handwritten Digit dataset,
accuracies of the privacy-sensitive digits are almost zero, while the
accuracies for the utility-relevant digits drop by 7.53% on average. This
presents a promising solution to the problem of privacy in machine learning for
classification.
Bellman Gradient Iteration for Inverse Reinforcement Learning
Authors: Kun Li, Yanan Sui, Joel W. Burdick
Date: 2017-07-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper develops an inverse reinforcement learning algorithm aimed at
recovering a reward function from the observed actions of an agent. We
introduce a strategy to flexibly handle different types of actions with two
approximations of the Bellman Optimality Equation, and a Bellman Gradient
Iteration method to compute the gradient of the Q-value with respect to the
reward function. These methods allow us to build a differentiable relation
between the Q-value and the reward function and learn an approximately optimal
reward function with gradient methods. We test the proposed method in two
simulated environments by evaluating the accuracy of different approximations
and comparing the proposed method with existing solutions. The results show
that even with a linear reward function, the proposed method has a comparable
accuracy with the state-of-the-art method adopting a non-linear reward
function, and the proposed method is more flexible because it is defined on
observed actions instead of trajectories.
Sparse Communication for Distributed Gradient Descent
Authors: Alham Fikri Aji, Kenneth Heafield
Date: 2017-04-17 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, image-classification, Image Classification, Machine Translation, NMT, Quantization, Translation

We make distributed stochastic gradient descent faster by exchanging sparse
updates instead of dense updates. Gradient updates are positively skewed as
most updates are near zero, so we map the 99% smallest updates (by absolute
value) to zero then exchange sparse matrices. This method can be combined with
quantization to further improve the compression. We explore different
configurations and apply them to neural machine translation and MNIST image
classification tasks. Most configurations work on MNIST, whereas different
configurations reduce convergence rate on the more complex translation task.
Our experiments show that we can achieve up to 49% speed up on MNIST and 22% on
NMT without damaging the final accuracy or BLEU.
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
Authors: Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian Ellis, Mohammad Ilyas, Nasir Rajpoot
Date: 2017-05-23 00:00:00
Tasks: Diagnostic, whole slide images

Evaluating expression of the Human epidermal growth factor receptor 2 (Her2)
by visual examination of immunohistochemistry (IHC) on invasive breast cancer
(BCa) is a key part of the diagnostic assessment of BCa due to its recognised
importance as a predictive and prognostic marker in clinical practice. However,
visual scoring of Her2 is subjective and consequently prone to inter-observer
variability. Given the prognostic and therapeutic implications of Her2 scoring,
a more objective method is required. In this paper, we report on a recent
automated Her2 scoring contest, held in conjunction with the annual PathSoc
meeting held in Nottingham in June 2016, aimed at systematically comparing and
advancing the state-of-the-art Artificial Intelligence (AI) based automated
methods for Her2 scoring. The contest dataset comprised of digitised whole
slide images (WSI) of sections from 86 cases of invasive breast carcinoma
stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting
algorithms automatically predicted scores of the IHC slides for an unseen
subset of the dataset and the predicted scores were compared with the 'ground
truth' (a consensus score from at least two experts). We also report on a
simple Man vs Machine contest for the scoring of Her2 and show that the
automated methods could beat the pathology experts on this contest dataset.
This paper presents a benchmark for comparing the performance of automated
algorithms for scoring of Her2. It also demonstrates the enormous potential of
automated algorithms in assisting the pathologist with objective IHC scoring.
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
Authors: Nicola Strisciuglio, George Azzopardi, Nicolai Petkov
Date: 2017-07-24 00:00:00
Tasks: Medical Image Analysis

The detection of curvilinear structures is an important step for various
computer vision applications, ranging from medical image analysis for
segmentation of blood vessels, to remote sensing for the identification of
roads and rivers, and to biometrics and robotics, among others. %The visual
system of the brain has remarkable abilities to detect curvilinear structures
in noisy images. This is a nontrivial task especially for the detection of thin
or incomplete curvilinear structures surrounded with noise. We propose a
general purpose curvilinear structure detector that uses the brain-inspired
trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear
filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis
thresholding and morphological closing. We demonstrate its effectiveness on a
data set of noisy images with cracked pavements, where we achieve
state-of-the-art results (F-measure=0.865). The proposed method can be employed
in any computer vision methodology that requires the delineation of curvilinear
and elongated structures.
Cross-domain Semantic Parsing via Paraphrasing
Authors: Yu Su, Xifeng Yan
Date: 2017-04-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Domain Adaptation, Semantic Parsing, Word Embeddings

Existing studies on semantic parsing mainly focus on the in-domain setting.
We formulate cross-domain semantic parsing as a domain adaptation problem:
train a semantic parser on some source domains and then adapt it to the target
domain. Due to the diversity of logical forms in different domains, this
problem presents unique and intriguing challenges. By converting logical forms
into canonical utterances in natural language, we reduce semantic parsing to
paraphrasing, and develop an attentive sequence-to-sequence paraphrase model
that is general and flexible to adapt to different domains. We discover two
problems, small micro variance and large macro variance, of pre-trained word
embeddings that hinder their direct use in neural networks, and propose
standardization techniques as a remedy. On the popular Overnight dataset, which
contains eight domains, we show that both cross-domain training and
standardized pre-trained word embeddings can bring significant improvement.
Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting
Authors: Amir Ghaderi, Borhan M. Sanandaji, Faezeh Ghaderi
Date: 2017-07-24 00:00:00
Tasks: Deep Learning, Spatio-Temporal Forecasting, Time Series, Time Series Analysis

The paper presents a spatio-temporal wind speed forecasting algorithm using
Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated
by recent advances in renewable energy integration and smart grids, we apply
our proposed algorithm for wind speed forecasting. Renewable energy resources
(wind and solar)are random in nature and, thus, their integration is
facilitated with accurate short-term forecasts. In our proposed framework, we
model the spatiotemporal information by a graph whose nodes are data generating
entities and its edges basically model how these nodes are interacting with
each other. One of the main contributions of our work is the fact that we
obtain forecasts of all nodes of the graph at the same time based on one
framework. Results of a case study on recorded time series data from a
collection of wind mills in the north-east of the U.S. show that the proposed
DL-based forecasting algorithm significantly improves the short-term forecasts
compared to a set of widely-used benchmarks models.
Guidelines for Artificial Intelligence Containment
Authors: James Babcock, Janos Kramar, Roman V. Yampolskiy
Date: 2017-07-24 00:00:00

With almost daily improvements in capabilities of artificial intelligence it
is more important than ever to develop safety software for use by the AI
research community. Building on our previous work on AI Containment Problem we
propose a number of guidelines which should help AI safety researchers to
develop reliable sandboxing software for intelligent programs of all levels.
Such safety container software will make it possible to study and analyze
intelligent artificial agent while maintaining certain level of safety against
information leakage, social engineering attacks and cyberattacks from within
the container.
Estimating the Number of Clusters via Normalized Cluster Instability
Authors: Jonas M. B. Haslbeck, Dirk U. Wulff
Date: 2016-08-26 00:00:00

We improve current instability-based methods for the selection of the number
of clusters $k$ in cluster analysis by developing a normalized cluster
instability measure that corrects for the distribution of cluster sizes, a
previously unaccounted driver of cluster instability. We show that our
normalized instability measure outperforms current instability-based measures
across the whole sequence of possible $k$ and especially overcomes limitations
in the context of large $k$. We also compare, for the first time, model-based
and model-free approaches to determine cluster-instability and find their
performance to be comparable. We make our method available in the R-package
\verb+cstab+.
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM
Authors: Felipe Gutierrez-Barragan, Vamsi K. Ithapu, Chris Hinrichs, Camille Maumet, Sterling C. Johnson, Thomas E. Nichols, Vikas Singh, the ADNI
Date: 2017-03-04 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

Permutation testing is a non-parametric method for obtaining the max null
distribution used to compute corrected $p$-values that provide strong control
of false positives. In neuroimaging, however, the computational burden of
running such an algorithm can be significant. We find that by viewing the
permutation testing procedure as the construction of a very large permutation
testing matrix, $T$, one can exploit structural properties derived from the
data and the test statistics to reduce the runtime under certain conditions. In
particular, we see that $T$ is low-rank plus a low-variance residual. This
makes $T$ a good candidate for low-rank matrix completion, where only a very
small number of entries of $T$ ($\sim0.35\%$ of all entries in our experiments)
have to be computed to obtain a good estimate. Based on this observation, we
present RapidPT, an algorithm that efficiently recovers the max null
distribution commonly obtained through regular permutation testing in
voxel-wise analysis. We present an extensive validation on a synthetic dataset
and four varying sized datasets against two baselines: Statistical
NonParametric Mapping (SnPM13) and a standard permutation testing
implementation (referred as NaivePT). We find that RapidPT achieves its best
runtime performance on medium sized datasets ($50 \leq n \leq 200$), with
speedups of 1.5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT). For larger
datasets ($n \geq 200$) RapidPT outperforms NaivePT (6x - 200x) on all
datasets, and provides large speedups over SnPM13 when more than 10000
permutations (2x - 15x) are needed. The implementation is a standalone toolbox
and also integrated within SnPM13, able to leverage multi-core architectures
when available.
Towards Real-Time Search Planning in Subsea Environments
Authors: James McMahon, Harun Yetkin, Artur Wolek, Zachary Waters, Dan Stilwell
Date: 2017-07-24 00:00:00

We address the challenge of computing search paths in real-time for subsea
applications where the goal is to locate an unknown number of targets on the
seafloor. Our approach maximizes a formal definition of search effectiveness
given finite search effort. We account for false positive measurements and
variation in the performance of the search sensor due to geographic variation
of the seafloor. We compare near-optimal search paths that can be computed in
real-time with optimal search paths for which real-time computation is
infeasible. We show how sonar data acquired for locating targets at a specific
location can also be used to characterize the performance of the search sonar
at that location. Our approach is illustrated with numerical experiments where
search paths are planned using sonar data previously acquired from Boston
Harbor.
Neural Module Networks
Authors: Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein
Date: 2015-11-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Visual Question Answering, Visual Question Answering (VQA)

Visual question answering is fundamentally compositional in nature---a
question like "where is the dog?" shares substructure with questions like "what
color is the dog?" and "where is the cat?" This paper seeks to simultaneously
exploit the representational capacity of deep networks and the compositional
linguistic structure of questions. We describe a procedure for constructing and
learning *neural module networks*, which compose collections of jointly-trained
neural "modules" into deep networks for question answering. Our approach
decomposes questions into their linguistic substructures, and uses these
structures to dynamically instantiate modular networks (with reusable
components for recognizing dogs, classifying colors, etc.). The resulting
compound networks are jointly trained. We evaluate our approach on two
challenging datasets for visual question answering, achieving state-of-the-art
results on both the VQA natural image dataset and a new dataset of complex
questions about abstract shapes.
Copy the dynamics using a learning machine
Authors: Hong Zhao
Date: 2017-07-24 00:00:00
Tasks: Time Series, Time Series Analysis

Is it possible to generally construct a dynamical system to simulate a black
system without recovering the equations of motion of the latter? Here we show
that this goal can be approached by a learning machine. Trained by a set of
input-output responses or a segment of time series of a black system, a
learning machine can be served as a copy system to mimic the dynamics of
various black systems. It can not only behave as the black system at the
parameter set that the training data are made, but also recur the evolution
history of the black system. As a result, the learning machine provides an
effective way for prediction, and enables one to probe the global dynamics of a
black system. These findings have significance for practical systems whose
equations of motion cannot be approached accurately. Examples of copying the
dynamics of an artificial neural network, the Lorenz system, and a variable
star are given. Our idea paves a possible way towards copy a living brain.
CharManteau: Character Embedding Models For Portmanteau Creation
Authors: Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy, Eric Nyberg
Date: 2017-07-04 00:00:00
Proceeding: EMNLP 2017 9

Portmanteaus are a word formation phenomenon where two words are combined to
form a new word. We propose character-level neural sequence-to-sequence (S2S)
methods for the task of portmanteau generation that are end-to-end-trainable,
language independent, and do not explicitly use additional phonetic
information. We propose a noisy-channel-style model, which allows for the
incorporation of unsupervised word lists, improving performance over a standard
source-to-target model. This model is made possible by an exhaustive candidate
generation strategy specifically enabled by the features of the portmanteau
task. Experiments find our approach superior to a state-of-the-art FST-based
baseline with respect to ground truth accuracy and human evaluation.
Deep Architectures for Neural Machine Translation
Authors: Antonio Valerio Miceli Barone, Jindřich Helcl, Rico Sennrich, Barry Haddow, Alexandra Birch
Date: 2017-07-24 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, GPU, Machine Translation, Translation

It has been shown that increasing model depth improves the quality of neural
machine translation. However, different architectural variants to increase
model depth have been proposed, and so far, there has been no thorough
comparative study.
  In this work, we describe and evaluate several existing approaches to
introduce depth in neural machine translation. Additionally, we explore novel
architectural variants, including deep transition RNNs, and we vary how
attention is used in the deep decoder. We introduce a novel "BiDeep" RNN
architecture that combines deep transition RNNs and stacked RNNs.
  Our evaluation is carried out on the English to German WMT news translation
dataset, using a single-GPU machine for both training and inference. We find
that several of our proposed architectures improve upon existing approaches in
terms of speed and translation quality. We obtain best improvements with a
BiDeep RNN of combined depth 8, obtaining an average improvement of 1.5 BLEU
over a strong shallow baseline.
  We release our code for ease of adoption.
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation
Authors: Yuanzhi Ke, Masafumi Hagiwara
Date: 2017-07-24 00:00:00
Tasks: General Classification, text-classification, Text Classification, Word Embeddings, Word Sense Disambiguation, Word Similarity

There have been some works that learn a lexicon together with the corpus to
improve the word embeddings. However, they either model the lexicon separately
but update the neural networks for both the corpus and the lexicon by the same
likelihood, or minimize the distance between all of the synonym pairs in the
lexicon. Such methods do not consider the relatedness and difference of the
corpus and the lexicon, and may not be the best optimized. In this paper, we
propose a novel method that considers the relatedness and difference of the
corpus and the lexicon. It trains word embeddings by learning the corpus to
predicate a word and its corresponding synonym under the context at the same
time. For polysemous words, we use a word sense disambiguation filter to
eliminate the synonyms that have different meanings for the context. To
evaluate the proposed method, we compare the performance of the word embeddings
trained by our proposed model, the control groups without the filter or the
lexicon, and the prior works in the word similarity tasks and text
classification task. The experimental results show that the proposed model
provides better embeddings for polysemous words and improves the performance
for text classification.
DE-PACRR: Exploring Layers Inside the PACRR Model
Authors: Andrew Yates, Kai Hui
Date: 2017-06-27 00:00:00
Tasks: Ad-Hoc Information Retrieval, Information Retrieval, model, Retrieval

Recent neural IR models have demonstrated deep learning's utility in ad-hoc
information retrieval. However, deep models have a reputation for being black
boxes, and the roles of a neural IR model's components may not be obvious at
first glance. In this work, we attempt to shed light on the inner workings of a
recently proposed neural IR model, namely the PACRR model, by visualizing the
output of intermediate layers and by investigating the relationship between
intermediate weights and the ultimate relevance score produced. We highlight
several insights, hoping that such insights will be generally applicable.
A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head
Authors: Sripad Krishna Devalla, Jean-Martial Mari, Tin A. Tun, Nicholas G. Strouthidis, Tin Aung, Alexandre H. Thiery, Michael J. A. Girard
Date: 2017-07-24 00:00:00
Tasks: Deep Learning, Management, Sensitivity, Specificity

Purpose: To develop a deep learning approach to digitally-stain optical
coherence tomography (OCT) images of the optic nerve head (ONH).
  Methods: A horizontal B-scan was acquired through the center of the ONH using
OCT (Spectralis) for 1 eye of each of 100 subjects (40 normal & 60 glaucoma).
All images were enhanced using adaptive compensation. A custom deep learning
network was then designed and trained with the compensated images to digitally
stain (i.e. highlight) 6 tissue layers of the ONH. The accuracy of our
algorithm was assessed (against manual segmentations) using the Dice
coefficient, sensitivity, and specificity. We further studied how compensation
and the number of training images affected the performance of our algorithm.
  Results: For images it had not yet assessed, our algorithm was able to
digitally stain the retinal nerve fiber layer + prelamina, the retinal pigment
epithelium, all other retinal layers, the choroid, and the peripapillary sclera
and lamina cribrosa. For all tissues, the mean dice coefficient was $0.84 \pm
0.03$, the mean sensitivity $0.92 \pm 0.03$, and the mean specificity $0.99 \pm
0.00$. Our algorithm performed significantly better when compensated images
were used for training. Increasing the number of images (from 10 to 40) to
train our algorithm did not significantly improve performance, except for the
RPE.
  Conclusion. Our deep learning algorithm can simultaneously stain neural and
connective tissues in ONH images. Our approach offers a framework to
automatically measure multiple key structural parameters of the ONH that may be
critical to improve glaucoma management.
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Authors: Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, Maarten de Rijke
Date: 2017-07-24 00:00:00
Tasks: Information Retrieval, Privacy Preserving, Re-Ranking, Retrieval

Deep neural networks have become a primary tool for solving problems in many
fields. They are also used for addressing information retrieval problems and
show strong performance in several tasks. Training these models requires large,
representative datasets and for most IR tasks, such data contains sensitive
information from users. Privacy and confidentiality concerns prevent many data
owners from sharing the data, thus today the research community can only
benefit from research on large-scale datasets in a limited manner. In this
paper, we discuss privacy preserving mimic learning, i.e., using predictions
from a privacy preserving trained model instead of labels from the original
sensitive training data as a supervision signal. We present the results of
preliminary experiments in which we apply the idea of mimic learning and
privacy preserving mimic learning for the task of document re-ranking as one of
the core IR tasks. This research is a step toward laying the ground for
enabling researchers from data-rich environments to share knowledge learned
from actual users' data, which should facilitate research collaborations.