pwc-archive/papers-with-abstracts huggingface dataset:
Disentangled VAE Representations for Multi-Aspect and Missing Data
Authors: Samuel K. Ainsworth, Nicholas J. Foti, Emily B. Fox
Date: 2018-06-24 00:00:00
Tasks: Disentanglement, Sentence

Many problems in machine learning and related application areas are
fundamentally variants of conditional modeling and sampling across multi-aspect
data, either multi-view, multi-modal, or simply multi-group. For example,
sampling from the distribution of English sentences conditioned on a given
French sentence or sampling audio waveforms conditioned on a given piece of
text. Central to many of these problems is the issue of missing data: we can
observe many English, French, or German sentences individually but only
occasionally do we have data for a sentence pair. Motivated by these
applications and inspired by recent progress in variational autoencoders for
grouped data, we develop factVAE, a deep generative model capable of handling
multi-aspect data, robust to missing observations, and with a prior that
encourages disentanglement between the groups and the latent dimensions. The
effectiveness of factVAE is demonstrated on a variety of rich real-world
datasets, including motion capture poses and pictures of faces captured from
varying poses and perspectives.
In-situ Stochastic Training of MTJ Crossbar based Neural Networks
Authors: Ankit Mondal, Ankur Srivastava
Date: 2018-06-24 00:00:00

Owing to high device density, scalability and non-volatility, Magnetic Tunnel
Junction-based crossbars have garnered significant interest for implementing
the weights of an artificial neural network. The existence of only two stable
states in MTJs implies a high overhead of obtaining optimal binary weights in
software. We illustrate that the inherent parallelism in the crossbar structure
makes it highly appropriate for in-situ training, wherein the network is taught
directly on the hardware. It leads to significantly smaller training overhead
as the training time is independent of the size of the network, while also
circumventing the effects of alternate current paths in the crossbar and
accounting for manufacturing variations in the device. We show how the
stochastic switching characteristics of MTJs can be leveraged to perform
probabilistic weight updates using the gradient descent algorithm. We describe
how the update operations can be performed on crossbars both with and without
access transistors and perform simulations on them to demonstrate the
effectiveness of our techniques. The results reveal that stochastically trained
MTJ-crossbar NNs achieve a classification accuracy nearly same as that of
real-valued-weight networks trained in software and exhibit immunity to device
variations.
A classification point-of-view about conditional Kendall's tau
Authors: Alexis Derumigny, Jean-David Fermanian
Date: 2018-06-23 00:00:00
Tasks: Classification, General Classification

We show how the problem of estimating conditional Kendall's tau can be
rewritten as a classification task. Conditional Kendall's tau is a conditional
dependence parameter that is a characteristic of a given pair of random
variables. The goal is to predict whether the pair is concordant (value of $1$)
or discordant (value of $-1$) conditionally on some covariates. We prove the
consistency and the asymptotic normality of a family of penalized approximate
maximum likelihood estimators, including the equivalent of the logit and probit
regressions in our framework. Then, we detail specific algorithms adapting
usual machine learning techniques, including nearest neighbors, decision trees,
random forests and neural networks, to the setting of the estimation of
conditional Kendall's tau. Finite sample properties of these estimators and
their sensitivities to each component of the data-generating process are
assessed in a simulation study. Finally, we apply all these estimators to a
dataset of European stock indices.
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
Authors: Thanh Hai Nguyen, Edi Prifti, Yann Chevaleyre, Nataliya Sokolovska, Jean-Daniel Zucker
Date: 2018-06-23 00:00:00
Tasks: Classification, Deep Learning, General Classification

Deep learning (DL) techniques have shown unprecedented success when applied
to images, waveforms, and text. Generally, when the sample size ($N$) is much
bigger than the number of features ($d$), DL often outperforms other machine
learning (ML) techniques, often through the use of Convolutional Neural
Networks (CNNs). However, in many bioinformatics fields (including
metagenomics), we encounter the opposite situation where $d$ is significantly
greater than $N$. In these situations, applying DL techniques would lead to
severe overfitting.
  Here we aim to improve classification of various diseases with metagenomic
data through the use of CNNs. For this we proposed to represent metagenomic
data as images. The proposed Met2Img approach relies on taxonomic and t-SNE
embeddings to transform abundance data into "synthetic images".
  We applied our approach to twelve benchmark data sets including more than
1400 metagenomic samples. Our results show significant improvements over the
state-of-the-art algorithms (Random Forest (RF), Support Vector Machine (SVM)).
We observe that the integration of phylogenetic information alongside abundance
data improves classification. The proposed approach is not only important in
classification setting but also allows to visualize complex metagenomic data.
The Met2Img is implemented in Python.
Variational Wasserstein Clustering
Authors: Liang Mi, Wen Zhang, Xianfeng GU, Yalin Wang
Date: 2018-06-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Clustering, Domain Adaptation, Representation Learning

We propose a new clustering method based on optimal transportation. We solve
optimal transportation with variational principles, and investigate the use of
power diagrams as transportation plans for aggregating arbitrary domains into a
fixed number of clusters. We iteratively drive centroids through target domains
while maintaining the minimum clustering energy by adjusting the power
diagrams. Thus, we simultaneously pursue clustering and the Wasserstein
distances between the centroids and the target domains, resulting in a
measure-preserving mapping. We demonstrate the use of our method in domain
adaptation, remeshing, and representation learning on synthetic and real data.
Parallel Transport Unfolding: A Connection-based Manifold Learning Approach
Authors: Max Budninskiy, Glorian Yin, Leman Feng, Yiying Tong, Mathieu Desbrun
Date: 2018-06-23 00:00:00
Tasks: Dimensionality Reduction

Manifold learning offers nonlinear dimensionality reduction of
high-dimensional datasets. In this paper, we bring geometry processing to bear
on manifold learning by introducing a new approach based on metric connection
for generating a quasi-isometric, low-dimensional mapping from a sparse and
irregular sampling of an arbitrary manifold embedded in a high-dimensional
space. Geodesic distances of discrete paths over the input pointset are
evaluated through "parallel transport unfolding" (PTU) to offer robustness to
poor sampling and arbitrary topology. Our new geometric procedure exhibits the
same strong resilience to noise as one of the staples of manifold learning, the
Isomap algorithm, as it also exploits all pairwise geodesic distances to
compute a low-dimensional embedding. While Isomap is limited to
geodesically-convex sampled domains, parallel transport unfolding does not
suffer from this crippling limitation, resulting in an improved robustness to
irregularity and voids in the sampling. Moreover, it involves only simple
linear algebra, significantly improves the accuracy of all pairwise geodesic
distance approximations, and has the same computational complexity as Isomap.
Finally, we show that our connection-based distance estimation can be used for
faster variants of Isomap such as L-Isomap.
Deductron -- A Recurrent Neural Network
Authors: Marek Rychlik
Date: 2018-06-23 00:00:00
Tasks: Image to text, Optical Character Recognition (OCR)

The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic. We constructed an example of structured data, motivated by problems from image-to-text conversion (OCR), which requires long-term memory to decode. Our data is a simple writing system, encoding characters 'X' and 'O' as their upper halves, which is possible due to symmetry of the two characters. The characters can be connected, as in some languages using cursive, such as Arabic (abjad). The string 'XOOXXO' may be encoded as '${\vee}{\wedge}\kern-1.5pt{\wedge}{\vee}\kern-1.5pt{\vee}{\wedge}$'. It follows that we may need to know arbitrarily long past to decode a current character, thus requiring long-term memory. Subsequently we constructed an RNN capable of decoding sequences encoded in this manner. Rather than by training, we constructed our RNN "by inspection", i.e. we guessed its weights. This involved a sequence of steps. We wrote a conventional program which decodes the sequences as the example above. Subsequently, we interpreted the program as a neural network (the only example of this kind known to us). Finally, we generalized this neural network to discover a new RNN architecture whose instance is our handcrafted RNN. It turns out to be a 3 layer network, where the middle layer is capable of performing simple logical inferences; thus the name "deductron". It is demonstrated that it is possible to train our network by simulated annealing. Also, known variants of stochastic gradient descent (SGD) methods are shown to work.
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions
Authors: Alex Kouzemtchenko
Date: 2018-06-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Malware Classification

There is a growing body of literature showing that deep neural networks are
vulnerable to adversarial input modification. Recently this work has been
extended from image classification to malware classification over boolean
features. In this paper we present several new methods for training restricted
networks in this specific domain that are highly effective at preventing
adversarial perturbations. We start with a fully adversarially resistant neural
network that has hard non-negative weight restrictions and is equivalent to
learning a monotonic boolean function and then attempt to relax the constraints
to improve classifier accuracy.
On Adversarial Examples for Character-Level Neural Machine Translation
Authors: Javid Ebrahimi, Daniel Lowd, Dejing Dou
Date: 2018-06-23 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, NMT, Translation

Evaluating on adversarial examples has become a standard procedure to measure
robustness of deep learning models. Due to the difficulty of creating white-box
adversarial examples for discrete text input, most analyses of the robustness
of NLP models have been done through black-box adversarial examples. We
investigate adversarial examples for character-level neural machine translation
(NMT), and contrast black-box adversaries with a novel white-box adversary,
which employs differentiable string-edit operations to rank adversarial
changes. We propose two novel types of attacks which aim to remove or change a
word in a translation, rather than simply break the NMT. We demonstrate that
white-box adversarial examples are significantly stronger than their black-box
counterparts in different attack scenarios, which show more serious
vulnerabilities than previously known. In addition, after performing
adversarial training, which takes only 3 times longer than regular training, we
can improve the model's robustness significantly.
Improving Text-to-SQL Evaluation Methodology
Authors: Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, Dragomir Radev
Date: 2018-06-23 00:00:00
Proceeding: ACL 2018 7
Tasks: SQL Parsing, Text to SQL, Text-To-SQL

To be informative, an evaluation must measure how well systems generalize to
realistic unseen data. We identify limitations of and propose improvements to
current evaluations of text-to-SQL systems. First, we compare human-generated
and automatically generated questions, characterizing properties of queries
necessary for real-world applications. To facilitate evaluation on multiple
datasets, we release standardized and improved versions of seven existing
datasets and one new text-to-SQL dataset. Second, we show that the current
division of data into training and test sets measures robustness to variations
in the way questions are asked, but only partially tests how well systems
generalize to new queries; therefore, we propose a complementary dataset split
for evaluation of future work. Finally, we demonstrate how the common practice
of anonymizing variables during evaluation removes an important challenge of
the task. Our observations highlight key difficulties, and our methodology
enables effective measurement of future development.
Considerations for a PAP Smear Image Analysis System with CNN Features
Authors: Srishti Gautam, Harinarayan K. K., Nirmal Jith, Anil K. Sao, Arnav Bhavsar, Adarsh Natarajan
Date: 2018-06-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Representation Learning, Segmentation, Transfer Learning

It has been shown that for automated PAP-smear image classification, nucleus
features can be very informative. Therefore, the primary step for automated
screening can be cell-nuclei detection followed by segmentation of nuclei in
the resulting single cell PAP-smear images. We propose a patch based approach
using CNN for segmentation of nuclei in single cell images. We then pose the
question of ion of segmentation for classification using representation
learning with CNN, and whether low-level CNN features may be useful for
classification. We suggest a CNN-based feature level analysis and a transfer
learning based approach for classification using both segmented as well full
single cell images. We also propose a decision-tree based approach for
classification. Experimental results demonstrate the effectiveness of the
proposed algorithms individually (with low-level CNN features), and
simultaneously proving the sufficiency of cell-nuclei detection (rather than
accurate segmentation) for classification. Thus, we propose a system for
analysis of multi-cell PAP-smear images consisting of a simple nuclei detection
algorithm followed by classification using transfer learning.
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching
Authors: Yao Zhou, Jingrui He
Date: 2018-06-23 00:00:00

The unprecedented demand for large amount of data has catalyzed the trend of
combining human insights with machine learning techniques, which facilitate the
use of crowdsourcing to enlist label information both effectively and
efficiently. The classic work on crowdsourcing mainly focuses on the label
inference problem under the categorization setting. However, inferring the true
label requires sophisticated aggregation models that usually can only perform
well under certain assumptions. Meanwhile, no matter how complicated the
aggregation model is, the true model that generated the crowd labels remains
unknown. Therefore, the label inference problem can never infer the ground
truth perfectly. Based on the fact that the crowdsourcing labels are abundant
and utilizing aggregation will lose such kind of rich annotation information
(e.g., which worker provided which labels), we believe that it is critical to
take the diverse labeling abilities of the crowdsourcing workers as well as
their correlations into consideration. To address the above challenge, we
propose to tackle three research problems, namely inference, learning, and
teaching.
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech
Authors: Gabrielle K. Liu
Date: 2018-06-23 00:00:00
Tasks: Classification, Emotion Recognition, General Classification, Speech Emotion Recognition, speech-recognition, Speech Recognition

Current approaches to speech emotion recognition focus on speech features
that can capture the emotional content of a speech signal. Mel Frequency
Cepstral Coefficients (MFCCs) are one of the most commonly used representations
for audio speech recognition and classification. This paper proposes Gammatone
Frequency Cepstral Coefficients (GFCCs) as a potentially better representation
of speech signals for emotion recognition. The effectiveness of MFCC and GFCC
representations are compared and evaluated over emotion and intensity
classification tasks with fully connected and recurrent neural network
architectures. The results provide evidence that GFCCs outperform MFCCs in
speech emotion recognition.
Leveraging Implicit Spatial Information in Global Features for Image Retrieval
Authors: Pierre Jacob, David Picard, Aymeric Histace, Edouard Klein
Date: 2018-06-23 00:00:00
Tasks: Image Retrieval, Retrieval

Most image retrieval methods use global features that aggregate local
distinctive patterns into a single representation. However, the aggregation
process destroys the relative spatial information by considering orderless sets
of local descriptors. We propose to integrate relative spatial information into
the aggregation process by taking into account co-occurrences of local patterns
in a tensor framework. The resulting signature called Improved Spatial Tensor
Aggregation (ISTA) is able to reach state of the art performances on well known
datasets such as Holidays, Oxford5k and Paris6k.
Stroke-based Character Reconstruction
Authors: Zhewei Huang, Wen Heng, Yuanzheng Tao, Shuchang Zhou
Date: 2018-06-23 00:00:00

Background elimination for noisy character images or character images from
real scene is still a challenging problem, due to the bewildering backgrounds,
uneven illumination, low resolution and different distortions. We propose a
stroke-based character reconstruction(SCR) method that use a weighted quadratic
Bezier curve(WQBC) to represent strokes of a character. Only training on our
synthetic data, our stroke extractor can achieve excellent reconstruction
effect in real scenes. Meanwhile. It can also help achieve great ability in
defending adversarial attacks of character recognizers.
An Improved Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search
Authors: Thomas Weise, Zijun Wu, Markus Wagner
Date: 2018-06-23 00:00:00

A commonly used strategy for improving optimization algorithms is to restart
the algorithm when it is believed to be trapped in an inferior part of the
search space. Building on the recent success of Bet-and-Run approaches for
restarted local search solvers, we introduce an improved generic Bet-and-Run
strategy. The goal is to obtain the best possible results within a given time
budget t using a given black-box optimization algorithm. If no prior knowledge
about problem features and algorithm behavior is available, the question about
how to use the time budget most efficiently arises. We propose to first start
k>=1 independent runs of the algorithm during an initialization budget t1<t,
pausing these runs, then apply a decision maker D to choose 1<=m<=k runs from
them (consuming t2>=0 time units in doing so), and then continuing these runs
for the remaining t3=t-t1-t2 time units. In previous Bet-and-Run strategies,
the decision maker D=currentBest would simply select the run with the best-
so-far results at negligible time. We propose using more advanced methods to
discriminate between "good" and "bad" sample runs, with the goal of increasing
the correlation of the chosen run with the a-posteriori best one. We test
several different approaches, including neural networks trained or polynomials
fitted on the current trace of the algorithm to predict which run may yield the
best results if granted the remaining budget. We show with extensive
experiments that this approach can yield better results than the previous
methods, but also find that the currentBest method is a very reliable and
robust baseline approach.
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
Authors: Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne
Date: 2018-06-23 00:00:00
Tasks: Segmentation

In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for the purpose of extracting trees. Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are used to construct the MHT tree, which is then traversed to make segmentation decisions. However, some critical parameters in this method are scale-dependent and have an adverse effect when tracking structures of varying dimensions. We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters. This enables our method to track trees starting from a single seed point. Our method is evaluated on chest CT data to extract airway trees and coronary arteries. In both cases, we show that our method performs significantly better than the original MHT method.
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System
Authors: Md Ashraful Alam Milton
Date: 2018-06-23 00:00:00
Tasks: Adversarial Attack, Face Recognition

The convolutional neural network is the crucial tool for the recent success
of deep learning based methods on various computer vision tasks like
classification, segmentation, and detection. Convolutional neural networks
achieved state-of-the-art performance in these tasks and every day pushing the
limit of computer vision and AI. However, adversarial attack on computer vision
systems is threatening their application in the real life and in
safety-critical applications. Necessarily, Finding adversarial examples are
important to detect susceptible models to attack and take safeguard measures to
overcome the adversarial attacks. In this regard, MCS 2018 Adversarial Attacks
on Black Box Face Recognition challenge aims to facilitate the research of
finding new adversarial attack techniques and their effectiveness in generating
adversarial examples. In this challenge, the attack"s nature is targeted-attack
on the black-box neural network where we have no knowledge about black-block"s
inner structure. The attacker must modify a set of five images of a single
person so that the neural network miss-classify them as target image which is a
set of five images of another person. In this competition, we applied Momentum
Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) to make an
adversarial attack on black-box face recognition system. We tested our method
on MCS 2018 Adversarial Attacks on Black Box Face Recognition challenge and
found competitive result. Our solution got validation score 1.404 which better
than baseline score 1.407 and stood 14 place among 132 teams in the
leader-board. Further improvement can be achieved by finding improved feature
extraction from source image, carefully chosen hyper-parameters, finding
improved substitute model of the black-box and better optimization method.