pwc-archive/papers-with-abstracts huggingface dataset:
Comparing Fairness Criteria Based on Social Outcome
Authors: Junpei Komiyama, Hajime Shimao
Date: 2018-06-13 00:00:00
Tasks: Decision Making, Fairness

Fairness in algorithmic decision-making processes is attracting increasing
concern. When an algorithm is applied to human-related decision-making an
estimator solely optimizing its predictive power can learn biases on the
existing data, which motivates us the notion of fairness in machine learning.
while several different notions are studied in the literature, little studies
are done on how these notions affect the individuals. We demonstrate such a
comparison between several policies induced by well-known fairness criteria,
including the color-blind (CB), the demographic parity (DP), and the equalized
odds (EO). We show that the EO is the only criterion among them that removes
group-level disparity. Empirical studies on the social welfare and disparity of
these policies are conducted.
Knowledge Amalgam: Generating Jokes and Quotes Together
Authors: Bhargav Chippada, Shubajit Saha
Date: 2018-06-12 00:00:00

Generating humor and quotes are very challenging problems in the field of
computational linguistics and are often tackled separately. In this paper, we
present a controlled Long Short-Term Memory (LSTM) architecture which is
trained with categorical data like jokes and quotes together by passing
category as an input along with the sequence of words. The idea is that a
single neural net will learn the structure of both jokes and quotes to generate
them on demand according to input category. Importantly, we believe the neural
net has more knowledge as it's trained on different datasets and hence will
enable it to generate more creative jokes or quotes from the mixture of
information. May the network generate a funny inspirational joke!
DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners
Authors: Frank G. Glavin, Michael G. Madden
Date: 2018-06-13 00:00:00

This paper describes an architecture for controlling non-player characters
(NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004.
Specifically, the DRE-Bot architecture is made up of three reinforcement
learners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda})
algorithm. This algorithm enables the NPC to learn through trial and error
building up experience over time in an approach inspired by human learning.
Experimentation is carried to measure the performance of DRE-Bot when competing
against fixed strategy bots that ship with the game. The discount parameter,
{\gamma}, and the trace parameter, {\lambda}, are also varied to see if their
values have an effect on the performance.
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
Authors: Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, Timo Dickscheid
Date: 2018-06-13 00:00:00

Cytoarchitectonic parcellations of the human brain serve as anatomical
references in multimodal atlas frameworks. They are based on analysis of
cell-body stained histological sections and the identification of borders
between brain areas. The de-facto standard involves a semi-automatic,
reproducible border detection, but does not scale with high-throughput imaging
in large series of sections at microscopical resolution. Automatic
parcellation, however, is extremely challenging due to high variation in the
data, and the need for a large field of view at microscopic resolution. The
performance of a recently proposed Convolutional Neural Network model that
addresses this problem especially suffers from the naturally limited amount of
expert annotations for training. To circumvent this limitation, we propose to
pre-train neural networks on a self-supervised auxiliary task, predicting the
3D distance between two patches sampled from the same brain. Compared to a
random initialization, fine-tuning from these networks results in significantly
better segmentations. We show that the self-supervised model has implicitly
learned to distinguish several cortical brain areas -- a strong indicator that
the proposed auxiliary task is appropriate for cytoarchitectonic mapping.
Graph-Based Decoding for Event Sequencing and Coreference Resolution
Authors: Zhengzhong Liu, Teruko Mitamura, Eduard Hovy
Date: 2018-06-13 00:00:00
Tasks: coreference-resolution, Coreference Resolution

Events in text documents are interrelated in complex ways. In this paper, we
study two types of relation: Event Coreference and Event Sequencing. We show
that the popular tree-like decoding structure for automated Event Coreference
is not suitable for Event Sequencing. To this end, we propose a graph-based
decoding algorithm that is applicable to both tasks. The new decoding algorithm
supports flexible feature sets for both tasks. Empirically, our event
coreference system has achieved state-of-the-art performance on the TAC-KBP
2015 event coreference task and our event sequencing system beats a strong
temporal-based, oracle-informed baseline. We discuss the challenges of studying
these event relations.
Spectral Network Embedding: A Fast and Scalable Method via Sparsity
Authors: Jie Zhang, Yan Wang, Jie Tang, Ming Ding
Date: 2018-06-07 00:00:00
Tasks: Link Prediction, Network Embedding, Node Classification

Network embedding aims to learn low-dimensional representations of nodes in a
network, while the network structure and inherent properties are preserved. It
has attracted tremendous attention recently due to significant progress in
downstream network learning tasks, such as node classification, link
prediction, and visualization. However, most existing network embedding methods
suffer from the expensive computations due to the large volume of networks. In
this paper, we propose a $10\times \sim 100\times$ faster network embedding
method, called Progle, by elegantly utilizing the sparsity property of online
networks and spectral analysis. In Progle, we first construct a \textit{sparse}
proximity matrix and train the network embedding efficiently via sparse matrix
decomposition. Then we introduce a network propagation pattern via spectral
analysis to incorporate local and global structure information into the
embedding. Besides, this model can be generalized to integrate network
information into other insufficiently trained embeddings at speed. Benefiting
from sparse spectral network embedding, our experiment on four different
datasets shows that Progle outperforms or is comparable to state-of-the-art
unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and
HOPE, regarding accuracy, while is $10\times$ faster than the fastest
word2vec-based method. Finally, we validate the scalability of Progle both in
real large-scale networks and multiple scales of synthetic networks.
Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reduction
Authors: Purushottam D. Dixit
Date: 2018-06-13 00:00:00
Tasks: Dimensionality Reduction

Stochastic kernel based dimensionality reduction approaches have become
popular in the last decade. The central component of many of these methods is a
symmetric kernel that quantifies the vicinity between pairs of data points and
a kernel-induced Markov chain on the data. Typically, the Markov chain is fully
specified by the kernel through row normalization. However, in many cases, it
is desirable to impose user-specified stationary-state and dynamical
constraints on the Markov chain. Unfortunately, no systematic framework exists
to impose such user-defined constraints. Here, we introduce a path entropy
maximization based approach to derive the transition probabilities of Markov
chains using a kernel and additional user-specified constraints. We illustrate
the usefulness of these Markov chains with examples.
Tempered Adversarial Networks
Authors: Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf
Date: 2018-02-12 00:00:00
Proceeding: ICML 2018 7

Generative adversarial networks (GANs) have been shown to produce realistic
samples from high-dimensional distributions, but training them is considered
hard. A possible explanation for training instabilities is the inherent
imbalance between the networks: While the discriminator is trained directly on
both real and fake samples, the generator only has control over the fake
samples it produces since the real data distribution is fixed by the choice of
a given dataset. We propose a simple modification that gives the generator
control over the real samples which leads to a tempered learning process for
both generator and discriminator. The real data distribution passes through a
lens before being revealed to the discriminator, balancing the generator and
discriminator by gradually revealing more detailed features necessary to
produce high-quality results. The proposed module automatically adjusts the
learning process to the current strength of the networks, yet is generic and
easy to add to any GAN variant. In a number of experiments, we show that this
can improve quality, stability and/or convergence speed across a range of
different GAN architectures (DCGAN, LSGAN, WGAN-GP).
Estimating Achilles tendon healing progress with convolutional neural networks
Authors: Norbert Kapinski, Jakub Zielinski, Bartosz A. Borucki, Tomasz Trzcinski, Beata Ciszkowska-Lyson, Krzysztof S. Nowinski
Date: 2018-06-13 00:00:00

Quantitative assessment of a treatment progress in the Achilles tendon
healing process - one of the most common musculoskeletal disorder in modern
medical practice - is typically a long and complex process: multiple MRI
protocols need to be acquired and analysed by radiology experts. In this paper,
we propose to significantly reduce the complexity of this assessment using a
novel method based on a pre-trained convolutional neural network. We first
train our neural network on over 500,000 2D axial cross-sections from over 3000
3D MRI studies to classify MRI images as belonging to a healthy or injured
class, depending on the patient's condition. We then take the outputs of
modified pre-trained network and apply linear regression on the PCA-reduced
space of the features to assess treatment progress. Our method allows to reduce
up to 5-fold the amount of data needed to be registered during the MRI scan
without any information loss. Furthermore, we are able to predict the healing
process phase with equal accuracy to human experts in 3 out of 6 main criteria.
Finally, contrary to the current approaches to regeneration assessment that
rely on radiologist subjective opinion, our method allows to objectively
compare different treatments methods which can lead to improved diagnostics and
patient's recovery.
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
Authors: Alexandre Bône, Olivier Colliot, Stanley Durrleman
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Hippocampus

We propose a method to learn a distribution of shape trajectories from
longitudinal data, i.e. the collection of individual objects repeatedly
observed at multiple time-points. The method allows to compute an average
spatiotemporal trajectory of shape changes at the group level, and the
individual variations of this trajectory both in terms of geometry and time
dynamics. First, we formulate a non-linear mixed-effects statistical model as
the combination of a generic statistical model for manifold-valued longitudinal
data, a deformation model defining shape trajectories via the action of a
finite-dimensional set of diffeomorphisms with a manifold structure, and an
efficient numerical scheme to compute parallel transport on this manifold.
Second, we introduce a MCMC-SAEM algorithm with a specific approach to shape
sampling, an adaptive scheme for proposal variances, and a log-likelihood
tempering strategy to estimate our model. Third, we validate our algorithm on
2D simulated data, and then estimate a scenario of alteration of the shape of
the hippocampus 3D brain structure during the course of Alzheimer's disease.
The method shows for instance that hippocampal atrophy progresses more quickly
in female subjects, and occurs earlier in APOE4 mutation carriers. We finally
illustrate the potential of our method for classifying pathological
trajectories versus normal ageing.
Group Equivariant Capsule Networks
Authors: Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski
Date: 2018-06-13 00:00:00
Proceeding: NeurIPS 2018 12

We present group equivariant capsule networks, a framework to introduce
guaranteed equivariance and invariance properties to the capsule network idea.
Our work can be divided into two contributions. First, we present a generic
routing by agreement algorithm defined on elements of a group and prove that
equivariance of output pose vectors, as well as invariance of output
activations, hold under certain conditions. Second, we connect the resulting
equivariant capsule networks with work from the field of group convolutional
networks. Through this connection, we provide intuitions of how both methods
relate and are able to combine the strengths of both approaches in one deep
neural network architecture. The resulting framework allows sparse evaluation
of the group convolution operator, provides control over specific equivariance
and invariance properties, and can use routing by agreement instead of pooling
operations. In addition, it is able to provide interpretable and equivariant
representation vectors as output capsules, which disentangle evidence of object
existence from its pose.
Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings
Authors: Jing-Yan Wang, Nihar B. Shah
Date: 2018-06-13 00:00:00

Cardinal scores (numeric ratings) collected from people are well known to
suffer from miscalibrations. A popular approach to address this issue is to
assume simplistic models of miscalibration (such as linear biases) to de-bias
the scores. This approach, however, often fares poorly because people's
miscalibrations are typically far more complex and not well understood. In the
absence of simplifying assumptions on the miscalibration, it is widely believed
by the crowdsourcing community that the only useful information in the cardinal
scores is the induced ranking. In this paper, inspired by the framework of
Stein's shrinkage, empirical Bayes, and the classic two-envelope problem, we
contest this widespread belief. Specifically, we consider cardinal scores with
arbitrary (or even adversarially chosen) miscalibrations which are only
required to be consistent with the induced ranking. We design estimators which
despite making no assumptions on the miscalibration, strictly and uniformly
outperform all possible estimators that rely on only the ranking. Our
estimators are flexible in that they can be used as a plug-in for a variety of
applications, and we provide a proof-of-concept for A/B testing and ranking.
Our results thus provide novel insights in the eternal debate between cardinal
and ordinal data.
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology
Authors: Heather D. Couture, J. S. Marron, Charles M. Perou, Melissa A. Troester, Marc Niethammer
Date: 2018-06-13 00:00:00
Tasks: Classification, General Classification, Image Augmentation, Multiple Instance Learning

Multiple instance (MI) learning with a convolutional neural network enables
end-to-end training in the presence of weak image-level labels. We propose a
new method for aggregating predictions from smaller regions of the image into
an image-level classification by using the quantile function. The quantile
function provides a more complete description of the heterogeneity within each
image, improving image-level classification. We also adapt image augmentation
to the MI framework by randomly selecting cropped regions on which to apply MI
aggregation during each epoch of training. This provides a mechanism to study
the importance of MI learning. We validate our method on five different
classification tasks for breast tumor histology and provide a visualization
method for interpreting local image classifications that could lead to future
insights into tumor heterogeneity.
Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces
Authors: Tatiana Tatarenko, Maryam Kamgarpour
Date: 2018-06-13 00:00:00

We consider online convex optimization with a zero-order oracle feedback. In particular, the decision maker does not know the explicit representation of the time-varying cost functions, or their gradients. At each time step, she observes the value of the corresponding cost function evaluated at her chosen action (zero-order oracle). The objective is to minimize the regret, that is, the difference between the sum of the costs she accumulates and that of a static optimal action had she known the sequence of cost functions a priori. We present a novel algorithm to minimize regret in unconstrained action spaces. Our algorithm hinges on a classical idea of one-point estimation of the gradients of the cost functions based on their observed values. The algorithm is independent of problem parameters. Letting $T$ denote the number of queries of the zero-order oracle and $n$ the problem dimension, the regret rate achieved is $O(n^{2/3}T^{2/3})$. Moreover, we adapt the presented algorithm to the setting with two-point feedback and demonstrate that the adapted procedure achieves the theoretical lower bound on the regret of $(n^{1/2}T^{1/2})$.
Error Bounds for Piecewise Smooth and Switching Regression
Authors: Fabien Lauer
Date: 2017-07-25 00:00:00
Tasks: regression

The paper deals with regression problems, in which the nonsmooth target is
assumed to switch between different operating modes. Specifically, piecewise
smooth (PWS) regression considers target functions switching deterministically
via a partition of the input space, while switching regression considers
arbitrary switching laws. The paper derives generalization error bounds in
these two settings by following the approach based on Rademacher complexities.
For PWS regression, our derivation involves a chaining argument and a
decomposition of the covering numbers of PWS classes in terms of the ones of
their component functions and the capacity of the classifier partitioning the
input space. This yields error bounds with a radical dependency on the number
of modes. For switching regression, the decomposition can be performed directly
at the level of the Rademacher complexities, which yields bounds with a linear
dependency on the number of modes. By using once more chaining and a
decomposition at the level of covering numbers, we show how to recover a
radical dependency. Examples of applications are given in particular for PWS
and swichting regression with linear and kernel-based component functions.
MAP inference via Block-Coordinate Frank-Wolfe Algorithm
Authors: Paul Swoboda, Vladimir Kolmogorov
Date: 2018-06-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Matching

We present a new proximal bundle method for Maximum-A-Posteriori (MAP)
inference in structured energy minimization problems. The method optimizes a
Lagrangean relaxation of the original energy minimization problem using a multi
plane block-coordinate Frank-Wolfe method that takes advantage of the specific
structure of the Lagrangean decomposition. We show empirically that our method
outperforms state-of-the-art Lagrangean decomposition based algorithms on some
challenging Markov Random Field, multi-label discrete tomography and graph
matching problems.
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
Authors: Di Kang, Zheng Ma, Antoni B. Chan
Date: 2017-05-29 00:00:00
Tasks: Density Estimation, regression

For crowded scenes, the accuracy of object-based computer vision methods
declines when the images are low-resolution and objects have severe occlusions.
Taking counting methods for example, almost all the recent state-of-the-art
counting methods bypass explicit detection and adopt regression-based methods
to directly count the objects of interest. Among regression-based methods,
density map estimation, where the number of objects inside a subregion is the
integral of the density map over that subregion, is especially promising
because it preserves spatial information, which makes it useful for both
counting and localization (detection and tracking). With the power of deep
convolutional neural networks (CNNs) the counting performance has improved
steadily. The goal of this paper is to evaluate density maps generated by
density estimation methods on a variety of crowd analysis tasks, including
counting, detection, and tracking. Most existing CNN methods produce density
maps with resolution that is smaller than the original images, due to the
downsample strides in the convolution/pooling operations. To produce an
original-resolution density map, we also evaluate a classical CNN that uses a
sliding window regressor to predict the density for every pixel in the image.
We also consider a fully convolutional (FCNN) adaptation, with skip connections
from lower convolutional layers to compensate for loss in spatial information
during upsampling. In our experiments, we found that the lower-resolution
density maps sometimes have better counting performance. In contrast, the
original-resolution density maps improved localization tasks, such as detection
and tracking, compared to bilinear upsampling the lower-resolution density
maps. Finally, we also propose several metrics for measuring the quality of a
density map, and relate them to experiment results on counting and
localization.
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation
Authors: Francis Grégoire, Philippe Langlais
Date: 2018-06-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Feature Engineering, Machine Translation, Sentence, Translation

Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose a
bidirectional recurrent neural network based approach to extract parallel
sentences from collections of multilingual texts. Our experiments with noisy
parallel corpora show that we can achieve promising results against a
competitive baseline by removing the need of specific feature engineering or
additional external resources. To justify the utility of our approach, we
extract sentence pairs from Wikipedia articles to train machine translation
systems and show significant improvements in translation performance.
A Probabilistic U-Net for Segmentation of Ambiguous Images
Authors: Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
Date: 2018-06-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making, Segmentation, Semantic Segmentation

Many real-world vision problems suffer from inherent ambiguities. In clinical
applications for example, it might not be clear from a CT scan alone which
particular region is cancer tissue. Therefore a group of graders typically
produces a set of diverse but plausible segmentations. We consider the task of
learning a distribution over segmentations given an input. To this end we
propose a generative segmentation model based on a combination of a U-Net with
a conditional variational autoencoder that is capable of efficiently producing
an unlimited number of plausible hypotheses. We show on a lung abnormalities
segmentation task and on a Cityscapes segmentation task that our model
reproduces the possible segmentation variants as well as the frequencies with
which they occur, doing so significantly better than published approaches.
These models could have a high impact in real-world applications, such as being
used as clinical decision-making algorithms accounting for multiple plausible
semantic segmentation hypotheses to provide possible diagnoses and recommend
further actions to resolve the present ambiguities.