pwc-archive/papers-with-abstracts huggingface dataset:
What About Applied Fairness?
Authors: Jared Sylvester, Edward Raff
Date: 2018-06-13 00:00:00
Tasks: Fairness, Position

Machine learning practitioners are often ambivalent about the ethical aspects
of their products. We believe anything that gets us from that current state to
one in which our systems are achieving some degree of fairness is an
improvement that should be welcomed. This is true even when that progress does
not get us 100% of the way to the goal of "complete" fairness or perfectly
align with our personal belief on which measure of fairness is used. Some
measure of fairness being built would still put us in a better position than
the status quo. Impediments to getting fairness and ethical concerns applied in
real applications, whether they are abstruse philosophical debates or technical
overhead such as the introduction of ever more hyper-parameters, should be
avoided. In this paper we further elaborate on our argument for this viewpoint
and its importance.
Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
Authors: Lorenz Berger, Eoin Hyde, Matt Gibb, Nevil Pavithran, Garin Kelly, Faiz Mumtaz, Sébastien Ourselin
Date: 2018-06-13 00:00:00
Tasks: Anatomy, Image Segmentation, Segmentation, Semantic Segmentation

Training deep neural networks on large and sparse datasets is still
challenging and can require large amounts of computation and memory. In this
work, we address the task of performing semantic segmentation on large
volumetric data sets, such as CT scans. Our contribution is threefold: 1) We
propose a boosted sampling scheme that uses a-posterior error maps, generated
throughout training, to focus sampling on difficult regions, resulting in a
more informative loss. This results in a significant training speed up and
improves learning performance for image segmentation. 2) We propose a novel
algorithm for boosting the SGD learning rate schedule by adaptively increasing
and lowering the learning rate, avoiding the need for extensive hyperparameter
tuning. 3) We show that our method is able to attain new state-of-the-art
results on the VISCERAL Anatomy benchmark.
Manifold Mixup: Better Representations by Interpolating Hidden States
Authors: Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio
Date: 2018-06-13 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Classification

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose Manifold Mixup, a simple regularizer that encourages neural networks to predict less confidently on interpolations of hidden representations. Manifold Mixup leverages semantic interpolations as additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance. We prove theory on why this flattening happens under ideal conditions, validate it on practical situations, and connect it to previous works on information theory and generalization. In spite of incurring no significant computation and being implemented in a few lines of code, Manifold Mixup improves strong baselines in supervised learning, robustness to single-step adversarial attacks, and test log-likelihood.
Understanding the Meaning of Understanding
Authors: Daniele Funaro
Date: 2018-06-13 00:00:00

Can we train a machine to detect if another machine has understood a concept?
In principle, this is possible by conducting tests on the subject of that
concept. However we want this procedure to be done by avoiding direct
questions. In other words, we would like to isolate the absolute meaning of an
abstract idea by putting it into a class of equivalence, hence without adopting
straight definitions or showing how this idea "works" in practice. We discuss
the metaphysical implications hidden in the above question, with the aim of
providing a plausible reference framework.
End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN
Authors: Soheil Esmaeilzadeh, Yao Yang, Ehsan Adeli
Date: 2018-06-13 00:00:00
Tasks: Deep Learning, General Classification, regression

In this work, we use a deep learning framework for simultaneous
classification and regression of Parkinson disease diagnosis based on MR-Images
and personal information (i.e. age, gender). We intend to facilitate and
increase the confidence in Parkinson disease diagnosis through our deep
learning framework.
Beyond Bags of Words: Inferring Systemic Nets
Authors: D. B. Skillicorn, N. Alsadhan
Date: 2018-06-13 00:00:00

Textual analytics based on representations of documents as bags of words have
been reasonably successful. However, analysis that requires deeper insight into
language, into author properties, or into the contexts in which documents were
created requires a richer representation. Systemic nets are one such
representation. They have not been extensively used because they required human
effort to construct. We show that systemic nets can be algorithmically inferred
from corpora, that the resulting nets are plausible, and that they can provide
practical benefits for knowledge discovery problems. This opens up a new class
of practical analysis techniques for textual analytics.
Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising
Authors: Zhihao Xia, Ayan Chakrabarti
Date: 2018-06-13 00:00:00
Tasks: Color Image Denoising, Denoising, Image Denoising, Image Restoration

Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. One recourse is to rely on "internal" image statistics, by searching for similar patterns within the input image itself. In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns. Given a pair of noisy patches, our network predicts whether different sub-band coefficients of the original noise-free patches are similar. The denoising algorithm then aggregates matched coefficients to obtain an initial estimate of the clean image. Finally, this estimate is provided as input, along with the original noisy image, to a standard regression-based denoising network. Experiments show that our method achieves state-of-the-art color image denoising performance, including with a blind version that trains a common model for a range of noise levels, and does not require knowledge of level of noise in an input image. Our approach also has a distinct advantage when training with limited amounts of training data.
3D-CODED : 3D Correspondences by Deep Deformation
Authors: Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
Date: 2018-06-13 00:00:00
Tasks: 3D Dense Shape Correspondence, 3D Human Pose Estimation, 3D Point Cloud Matching, 3D Surface Generation

We present a new deep learning approach for matching deformable shapes by
introducing {\it Shape Deformation Networks} which jointly encode 3D shapes and
correspondences. This is achieved by factoring the surface representation into
(i) a template, that parameterizes the surface, and (ii) a learnt global
feature vector that parameterizes the transformation of the template into the
input surface. By predicting this feature for a new shape, we implicitly
predict correspondences between this shape and the template. We show that these
correspondences can be improved by an additional step which improves the shape
feature by minimizing the Chamfer distance between the input and transformed
template. We demonstrate that our simple approach improves on state-of-the-art
results on the difficult FAUST-inter challenge, with an average correspondence
error of 2.88cm. We show, on the TOSCA dataset, that our method is robust to
many types of perturbations, and generalizes to non-human shapes. This
robustness allows it to perform well on real unclean, meshes from the the SCAPE
dataset.
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art
Authors: Artur Jordao, Antonio C. Nazare Jr., Jessica Sena, William Robson Schwartz
Date: 2018-06-13 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, image-classification, Image Classification, object-detection, Object Detection

Human activity recognition based on wearable sensor data has been an
attractive research topic due to its application in areas such as healthcare
and smart environments. In this context, many works have presented remarkable
results using accelerometer, gyroscope and magnetometer data to represent the
activities categories. However, current studies do not consider important
issues that lead to skewed results, making it hard to assess the quality of
sensor-based human activity recognition and preventing a direct comparison of
previous works. These issues include the samples generation processes and the
validation protocols used. We emphasize that in other research areas, such as
image classification and object detection, these issues are already
well-defined, which brings more efforts towards the application. Inspired by
this, we conduct an extensive set of experiments that analyze different sample
generation processes and validation protocols to indicate the vulnerable points
in human activity recognition based on wearable sensor data. For this purpose,
we implement and evaluate several top-performance methods, ranging from
handcrafted-based approaches to convolutional neural networks. According to our
study, most of the experimental evaluations that are currently employed are not
adequate to perform the activity recognition in the context of wearable sensor
data, in which the recognition accuracy drops considerably when compared to an
appropriate evaluation approach. To the best of our knowledge, this is the
first study that tackles essential issues that compromise the understanding of
the performance in human activity recognition based on wearable sensor data.
Learning Spontaneity to Improve Emotion Recognition In Speech
Authors: Karttikeya Mangalam, Tanaya Guha
Date: 2017-12-12 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

We investigate the effect and usefulness of spontaneity (i.e. whether a given
speech is spontaneous or not) in speech in the context of emotion recognition.
We hypothesize that emotional content in speech is interrelated with its
spontaneity, and use spontaneity classification as an auxiliary task to the
problem of emotion recognition. We propose two supervised learning settings
that utilize spontaneity to improve speech emotion recognition: a hierarchical
model that performs spontaneity detection before performing emotion
recognition, and a multitask learning model that jointly learns to recognize
both spontaneity and emotion. Through various experiments on the well known
IEMOCAP database, we show that by using spontaneity detection as an additional
task, significant improvement can be achieved over emotion recognition systems
that are unaware of spontaneity. We achieve state-of-the-art emotion
recognition accuracy (4-class, 69.1%) on the IEMOCAP database outperforming
several relevant and competitive baselines.
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis
Authors: Andrew Moore, Paul Rayson
Date: 2018-06-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis

Lack of repeatability and generalisability are two significant threats to
continuing scientific development in Natural Language Processing. Language
models and learning methods are so complex that scientific conference papers no
longer contain enough space for the technical depth required for replication or
reproduction. Taking Target Dependent Sentiment Analysis as a case study, we
show how recent work in the field has not consistently released code, or
described settings for learning methods in enough detail, and lacks
comparability and generalisability in train, test or validation data. To
investigate generalisability and to enable state of the art comparative
evaluations, we carry out the first reproduction studies of three groups of
complementary methods and perform the first large-scale mass evaluation on six
different English datasets. Reflecting on our experiences, we recommend that
future replication or reproduction experiments should always consider a variety
of datasets alongside documenting and releasing their methods and published
code in order to minimise the barriers to both repeatability and
generalisability. We have released our code with a model zoo on GitHub with
Jupyter Notebooks to aid understanding and full documentation, and we recommend
that others do the same with their papers at submission time through an
anonymised GitHub account.
Impostor Networks for Fast Fine-Grained Recognition
Authors: Vadim Lebedev, Artem Babenko, Victor Lempitsky
Date: 2018-06-13 00:00:00
Tasks: GPU

In this work we introduce impostor networks, an architecture that allows to
perform fine-grained recognition with high accuracy and using a light-weight
convolutional network, making it particularly suitable for fine-grained
applications on low-power and non-GPU enabled platforms. Impostor networks
compensate for the lightness of its `backend' network by combining it with a
lightweight non-parametric classifier. The combination of a convolutional
network and such non-parametric classifier is trained in an end-to-end fashion.
Similarly to convolutional neural networks, impostor networks can fit
large-scale training datasets very well, while also being able to generalize to
new data points. At the same time, the bulk of computations within impostor
networks happen through nearest neighbor search in high-dimensions. Such search
can be performed efficiently on a variety of architectures including standard
CPUs, where deep convolutional networks are inefficient. In a series of
experiments with three fine-grained datasets, we show that impostor networks
are able to boost the classification accuracy of a moderate-sized convolutional
network considerably at a very small computational cost.
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Authors: Gongbo Tang, Fabienne Cap, Eva Pettersson, Joakim Nivre
Date: 2018-06-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, NMT, Translation

In this paper, we apply different NMT models to the problem of historical
spelling normalization for five languages: English, German, Hungarian,
Icelandic, and Swedish. The NMT models are at different levels, have different
attention mechanisms, and different neural network architectures. Our results
show that NMT models are much better than SMT models in terms of character
error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical
spelling normalization. Transformer models perform better only when provided
with more training data. We also find that subword-level models with a small
subword vocabulary are better than character-level models for low-resource
languages. In addition, we propose a hybrid method which further improves the
performance of historical spelling normalization.
Offline Evaluation of Ranking Policies with Click Models
Authors: Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S. Muthukrishnan, Vishwa Vinay, Zheng Wen
Date: 2018-04-27 00:00:00
Tasks: Recommendation Systems

Many web systems rank and present a list of items to users, from recommender
systems to search and advertising. An important problem in practice is to
evaluate new ranking policies offline and optimize them before they are
deployed. We address this problem by proposing evaluation algorithms for
estimating the expected number of clicks on ranked lists from historical logged
data. The existing algorithms are not guaranteed to be statistically efficient
in our problem because the number of recommended lists can grow exponentially
with their length. To overcome this challenge, we use models of user
interaction with the list of items, the so-called click models, to construct
estimators that learn statistically efficiently. We analyze our estimators and
prove that they are more efficient than the estimators that do not use the
structure of the click model, under the assumption that the click model holds.
We evaluate our estimators in a series of experiments on a real-world dataset
and show that they consistently outperform prior estimators.
Skeletracks: automatic separation of overlapping fission tracks in apatite and muscovite using image processing
Authors: Alexandre Fioravante de Siqueira, Wagner Massayuki Nakasuga, Sandro Guedes
Date: 2018-06-13 00:00:00
Tasks: Binarization

One of the major difficulties of automatic track counting using
photomicrographs is separating overlapped tracks. We address this issue
combining image processing algorithms such as skeletonization, and we test our
algorithm with several binarization techniques. The counting algorithm was
successfully applied to determine the efficiency factor GQR, necessary for
standardless fission-track dating, involving counting induced tracks in apatite
and muscovite with superficial densities of about $6 \times 10^5$
tracks/$cm^2$.
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
Authors: Benjamin Baron, Mirco Musolesi
Date: 2017-10-23 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning, Privacy Preserving

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy.
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Authors: Mikhail Belkin, Daniel Hsu, Partha Mitra
Date: 2018-06-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: BIG-bench Machine Learning, General Classification, regression

Many modern machine learning models are trained to achieve zero or near-zero
training error in order to obtain near-optimal (but non-zero) test error. This
phenomenon of strong generalization performance for "overfitted" / interpolated
classifiers appears to be ubiquitous in high-dimensional data, having been
observed in deep networks, kernel machines, boosting and random forests. Their
performance is consistently robust even when the data contain large amounts of
label noise.
  Very little theory is available to explain these observations. The vast
majority of theoretical analyses of generalization allows for interpolation
only when there is little or no label noise. This paper takes a step toward a
theoretical foundation for interpolated classifiers by analyzing local
interpolating schemes, including geometric simplicial interpolation algorithm
and singularly weighted $k$-nearest neighbor schemes. Consistency or
near-consistency is proved for these schemes in classification and regression
problems. Moreover, the nearest neighbor schemes exhibit optimal rates under
some standard statistical assumptions.
  Finally, this paper suggests a way to explain the phenomenon of adversarial
examples, which are seemingly ubiquitous in modern machine learning, and also
discusses some connections to kernel machines and random forests in the
interpolated regime.
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
Authors: Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt, Tuo Zhao
Date: 2018-06-13 00:00:00
Tasks: Generalization Bounds

We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the networks. Through introducing a new characterization of the Lipschitz properties of neural network family, we achieve significantly tighter generalization bounds than existing results. Moreover, we show that the generalization bound can be further improved for bounded losses. Aside from the general feedforward deep neural networks, our results can be applied to derive new bounds for popular architectures, including convolutional neural networks (CNNs) and residual networks (ResNets). When achieving same generalization errors with previous arts, our bounds allow for the choice of larger parameter spaces of weight matrices, inducing potentially stronger expressive ability for neural networks. Numerical evaluation is also provided to support our theory.
Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
Authors: Evangelos B. Mazomenos, Kamakshi Bansal, Bruce Martin, Andrew Smith, Susan Wright, Danail Stoyanov
Date: 2018-06-13 00:00:00
Tasks: Diagnostic

Transoesophageal echocardiography (TEE) is a valuable diagnostic and
monitoring imaging modality. Proper image acquisition is essential for
diagnosis, yet current assessment techniques are solely based on manual expert
review. This paper presents a supervised deep learn ing framework for
automatically evaluating and grading the quality of TEE images. To obtain the
necessary dataset, 38 participants of varied experience performed TEE exams
with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural
Network (CNN) architectures, AlexNet and VGG, structured to perform regression,
were finetuned and validated on manually graded images from three evaluators.
Two different scoring strategies, a criteria-based percentage and an overall
general impression, were used. The developed CNN models estimate the average
score with a root mean square accuracy ranging between 84%-93%, indicating the
ability to replicate expert valuation. Proposed strategies for automated TEE
assessment can have a significant impact on the training process of new TEE
operators, providing direct feedback and facilitating the development of the
necessary dexterous skills.
Bandits with Delayed, Aggregated Anonymous Feedback
Authors: Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen Grunewalder
Date: 2017-09-20 00:00:00
Proceeding: ICML 2018 7

We study a variant of the stochastic $K$-armed bandit problem, which we call
"bandits with delayed, aggregated anonymous feedback". In this problem, when
the player pulls an arm, a reward is generated, however it is not immediately
observed. Instead, at the end of each round the player observes only the sum of
a number of previously generated rewards which happen to arrive in the given
round. The rewards are stochastically delayed and due to the aggregated nature
of the observations, the information of which arm led to a particular reward is
lost. The question is what is the cost of the information loss due to this
delayed, aggregated anonymous feedback? Previous works have studied bandits
with stochastic, non-anonymous delays and found that the regret increases only
by an additive factor relating to the expected delay. In this paper, we show
that this additive regret increase can be maintained in the harder delayed,
aggregated anonymous feedback setting when the expected delay (or a bound on
it) is known. We provide an algorithm that matches the worst case regret of the
non-anonymous problem exactly when the delays are bounded, and up to
logarithmic factors or an additive variance term for unbounded delays.