pwc-archive/papers-with-abstracts huggingface dataset:
On the Adversarial Robustness of Multivariate Robust Estimation
Authors: Erhan Bayraktar, Lifeng Lai
Date: 2019-03-27 00:00:00
Tasks: Adversarial Robustness

In this paper, we investigate the adversarial robustness of multivariate
$M$-Estimators. In the considered model, after observing the whole dataset, an
adversary can modify all data points with the goal of maximizing inference
errors. We use adversarial influence function (AIF) to measure the asymptotic
rate at which the adversary can change the inference result. We first
characterize the adversary's optimal modification strategy and its
corresponding AIF. From the defender's perspective, we would like to design an
estimator that has a small AIF. For the case of joint location and scale
estimation problem, we characterize the optimal $M$-estimator that has the
smallest AIF. We further identify a tradeoff between robustness against
adversarial modifications and robustness against outliers, and derive the
optimal $M$-estimator that achieves the best tradeoff.
Neural-networks for geophysicists and their application to seismic data interpretation
Authors: Bas Peters, Eldad Haber, Justin Granek
Date: 2019-03-27 00:00:00

Neural-networks have seen a surge of interest for the interpretation of
seismic images during the last few years. Network-based learning methods can
provide fast and accurate automatic interpretation, provided there are
sufficiently many training labels. We provide an introduction to the field
aimed at geophysicists that are familiar with the framework of forward modeling
and inversion. We explain the similarities and differences between deep
networks to other geophysical inverse problems and show their utility in
solving problems such as lithology interpolation between wells, horizon
tracking and segmentation of seismic images. The benefits of our approach are
demonstrated on field data from the Sea of Ireland and the North Sea.
Colorectal cancer diagnosis from histology images: A comparative study
Authors: Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth, Turker Ince, Somaya Al-Maadeed, Ridha Hamila, Moncef Gabbouj
Date: 2019-03-27 00:00:00
Tasks: Transfer Learning

Computer-aided diagnosis (CAD) based on histopathological imaging has
progressed rapidly in recent years with the rise of machine learning based
methodologies. Traditional approaches consist of training a classification
model using features extracted from the images, based on textures or
morphological properties. Recently, deep-learning based methods have been
applied directly to the raw (unprocessed) data. However, their usability is
impacted by the paucity of annotated data in the biomedical sector. In order to
leverage the learning capabilities of deep Convolutional Neural Nets (CNNs)
within the confines of limited labelled data, in this study we shall
investigate the transfer learning approaches that aim to apply the knowledge
gained from solving a source (e.g., non-medical) problem, to learn better
predictive models for the target (e.g., biomedical) task. As an alternative, we
shall further propose a new adaptive and compact CNN based architecture that
can be trained from scratch even on scarce and low-resolution data. Moreover,
we conduct quantitative comparative evaluations among the traditional methods,
transfer learning-based methods and the proposed adaptive approach for the
particular task of cancer detection and identification from scarce and
low-resolution histology images. Over the largest benchmark dataset formed for
this purpose, the proposed adaptive approach achieved a higher cancer detection
accuracy with a significant gap, whereas the deep CNNs with transfer learning
achieved a superior cancer identification.
Information Maximizing Visual Question Generation
Authors: Ranjay Krishna, Michael Bernstein, Li Fei-Fei
Date: 2019-03-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Question Generation, Question-Generation

Though image-to-sequence generation models have become overwhelmingly popular
in human-computer communications, they suffer from strongly favoring safe
generic questions ("What is in this picture?"). Generating uninformative but
relevant questions is not sufficient or useful. We argue that a good question
is one that has a tightly focused purpose --- one that is aimed at expecting a
specific type of response. We build a model that maximizes mutual information
between the image, the expected answer and the generated question. To overcome
the non-differentiability of discrete natural language tokens, we introduce a
variational continuous latent space onto which the expected answers project. We
regularize this latent space with a second latent space that ensures clustering
of similar answers. Even when we don't know the expected answer, this second
latent space can generate goal-driven questions specifically aimed at
extracting objects ("what is the person throwing"), attributes, ("What kind of
shirt is the person wearing?"), color ("what color is the frisbee?"), material
("What material is the frisbee?"), etc. We quantitatively show that our model
is able to retain information about an expected answer category, resulting in
more diverse, goal-driven questions. We launch our model on a set of real world
images and extract previously unseen visual concepts.
Kernel based regression with robust loss function via iteratively reweighted least squares
Authors: Hongwei Dong, Liming Yang
Date: 2019-03-27 00:00:00
Tasks: regression

Least squares kernel based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called $\ell_s$-loss, is proposed in this paper. With the support of novel loss function, two kernel based regressors are constructed by replacing the $\ell_2$-loss in LS-SVR and ELM with the proposed $\ell_s$-loss for better noise robustness. Important properties of $\ell_s$-loss, including robustness, asymmetry and asymptotic approximation behaviors, are verified theoretically. Moreover, iteratively reweighted least squares (IRLS) is utilized to optimize and interpret the proposed methods from a weighted viewpoint. The convergence of the proposal are proved, and detailed analyses of robustness are given. Experiments on both artificial and benchmark datasets confirm the validity of the proposed methods.
Localized Linear Regression in Networked Data
Authors: Alexander Jung, Nguyen Tran
Date: 2019-03-26 00:00:00
Tasks: regression

The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (generalized) linear models to network models. Efficient implementations of the nLasso have been obtained using convex optimization methods lending to scalable message passing protocols. In this paper, we analyze the statistical properties of nLasso when applied to localized linear regression problems involving networked data. Our main result is a sufficient condition on the network structure and available label information such that nLasso accurately learns a localized linear regression model from a few labeled data points. We also provide an implementation of nLasso for localized linear regression by specializing a primaldual method for solving the convex (non-smooth) nLasso problem.
On evaluating CNN representations for low resource medical image classification
Authors: Taruna Agrawal, Rahul Gupta, Shrikanth Narayanan
Date: 2019-03-26 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Keyword Spotting, Medical Image Classification, Object Tracking, Transfer Learning

Convolutional Neural Networks (CNNs) have revolutionized performances in
several machine learning tasks such as image classification, object tracking,
and keyword spotting. However, given that they contain a large number of
parameters, their direct applicability into low resource tasks is not
straightforward. In this work, we experiment with an application of CNN models
to gastrointestinal landmark classification with only a few thousands of
training samples through transfer learning. As in a standard transfer learning
approach, we train CNNs on a large external corpus, followed by representation
extraction for the medical images. Finally, a classifier is trained on these
CNN representations. However, given that several variants of CNNs exist, the
choice of CNN is not obvious. To address this, we develop a novel metric that
can be used to predict test performances, given CNN representations on the
training set. Not only we demonstrate the superiority of the CNN based transfer
learning approach against an assembly of knowledge driven features, but the
proposed metric also carries an 87% correlation with the test set performances
as obtained using various CNN representations.
Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression
Authors: Wenshan Wang, Aayush Ahuja, Yanfu Zhang, Rogerio Bonatti, Sebastian Scherer
Date: 2019-03-26 00:00:00
Tasks: regression

In the task of Autonomous aerial filming of a moving actor (e.g. a person or
a vehicle), it is crucial to have a good heading direction estimation for the
actor from the visual input. However, the models obtained in other similar
tasks, such as pedestrian collision risk analysis and human-robot interaction,
are very difficult to generalize to the aerial filming task, because of the
difference in data distributions. Towards improving generalization with less
amount of labeled data, this paper presents a semi-supervised algorithm for
heading direction estimation problem. We utilize temporal continuity as the
unsupervised signal to regularize the model and achieve better generalization
ability. This semi-supervised algorithm is applied to both training and testing
phases, which increases the testing performance by a large margin. We show that
by leveraging unlabeled sequences, the amount of labeled data required can be
significantly reduced. We also discuss several important details on improving
the performance by balancing labeled and unlabeled loss, and making good
combinations. Experimental results show that our approach robustly outputs the
heading direction for different types of actor. The aesthetic value of the
video is also improved in the aerial filming task.
Weighted Multisource Tradaboost
Authors: João Antunes, Alexandre Bernardino, Asim Smailagic, Daniel Siewiorek
Date: 2019-03-26 00:00:00
Tasks: Transfer Learning

In this paper we propose an improved method for transfer learning that takes
into account the balance between target and source data. This method builds on
the state-of-the-art Multisource Tradaboost, but weighs the importance of each
datapoint taking into account the amount of target and source data available. A
comparative study is then presented exposing the performance of four transfer
learning methods as well as the proposed Weighted Multisource Tradaboost. The
experimental results show that the proposed method is able to outperform the
base method as the number of target samples increase. These results are
promising in the sense that source-target ratio weighing may be a path to
improve current methods of transfer learning. However, against the asymptotic
conjecture, all transfer learning methods tested in this work get outperformed
by a no-transfer SVM for large number on target samples.
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
Authors: Felix Petersen, Amit H. Bermano, Oliver Deussen, Daniel Cohen-Or
Date: 2019-03-26 00:00:00
Tasks: 3D geometry, 3D Object Reconstruction, Domain Adaptation, Image-to-Image Translation, Object Reconstruction, Translation

The long-coveted task of reconstructing 3D geometry from images is still a standing problem. In this paper, we build on the power of neural networks and introduce Pix2Vex, a network trained to convert camera-captured images into 3D geometry. We present a novel differentiable renderer ($DR$) as a forward validation means during training. Our key insight is that $DR$s produce images of a particular appearance, different from typical input images. Hence, we propose adding an image-to-image translation component, converting between these rendering styles. This translation closes the training loop, while allowing to use minimal supervision only, without needing any 3D model as ground truth. Unlike state-of-the-art methods, our $DR$ is $C^\infty$ smooth and thus does not display any discontinuities at occlusions or dis-occlusions. Through our novel training scheme, our network can train on different types of images, where previous work can typically only train on images of a similar appearance to those rendered by a $DR$.
Hearing your touch: A new acoustic side channel on smartphones
Authors: Ilia Shumailov, Laurent Simon, Jeff Yan, Ross Anderson
Date: 2019-03-26 00:00:00

We present the first acoustic side-channel attack that recovers what users
type on the virtual keyboard of their touch-screen smartphone or tablet. When a
user taps the screen with a finger, the tap generates a sound wave that
propagates on the screen surface and in the air. We found the device's
microphone(s) can recover this wave and "hear" the finger's touch, and the
wave's distortions are characteristic of the tap's location on the screen.
Hence, by recording audio through the built-in microphone(s), a malicious app
can infer text as the user enters it on their device. We evaluate the
effectiveness of the attack with 45 participants in a real-world environment on
an Android tablet and an Android smartphone. For the tablet, we recover 61% of
200 4-digit PIN-codes within 20 attempts, even if the model is not trained with
the victim's data. For the smartphone, we recover 9 words of size 7--13 letters
with 50 attempts in a common side-channel attack benchmark. Our results suggest
that it not always sufficient to rely on isolation mechanisms such as TrustZone
to protect user input. We propose and discuss hardware, operating-system and
application-level mechanisms to block this attack more effectively. Mobile
devices may need a richer capability model, a more user-friendly notification
system for sensor usage and a more thorough evaluation of the information
leaked by the underlying hardware.
SuSi: Supervised Self-Organizing Maps for Regression and Classification in Python
Authors: Felix M. Riese, Sina Keller
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need for machine learning techniques which are well-suited for these different datasets. One possible technique is the self-organizing map (SOM), a type of artificial neural network which is, so far, weakly represented in the field of machine learning. The SOM's unique characteristic is the neighborhood relationship of the output neurons. This relationship improves the ability of generalization on small datasets. SOMs are mostly applied in unsupervised learning and few studies focus on using SOMs as supervised learning approach. Furthermore, no appropriate SOM package is available with respect to machine learning standards and in the widely used programming language Python. In this paper, we introduce the freely available Supervised Self-organizing maps (SuSi) Python package which performs supervised regression and classification. The implementation of SuSi is described with respect to the underlying mathematics. Then, we present first evaluations of the SOM for regression and classification datasets from two different domains of geospatial image analysis. Despite the early stage of its development, the SuSi framework performs well and is characterized by only small performance differences between the training and the test datasets. A comparison of the SuSi framework with existing Python and R packages demonstrates the importance of the SuSi framework. In future work, the SuSi framework will be extended, optimized and upgraded e.g. with tools to better understand and visualize the input data as well as the handling of missing and incomplete data.
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Authors: Oluwaseyi Feyisetan, Thomas Drake, Borja Balle, Tom Diethe
Date: 2019-03-26 00:00:00
Tasks: Active Learning, Binary Classification, General Classification, intent-classification, Intent Classification, Privacy Preserving

Active learning holds promise of significantly reducing data annotation costs
while maintaining reasonable model performance. However, it requires sending
data to annotators for labeling. This presents a possible privacy leak when the
training set includes sensitive user data. In this paper, we describe an
approach for carrying out privacy preserving active learning with quantifiable
guarantees. We evaluate our approach by showing the tradeoff between privacy,
utility and annotation budget on a binary classification task in a active
learning setting.
Energy Storage Management via Deep Q-Networks
Authors: Ahmed S. Zamzam, Bo Yang, Nicholas D. Sidiropoulos
Date: 2019-03-26 00:00:00
Tasks: Management, Reinforcement Learning, Reinforcement Learning (RL)

Energy storage devices represent environmentally friendly candidates to cope
with volatile renewable energy generation. Motivated by the increase in
privately owned storage systems, this paper studies the problem of real-time
control of a storage unit co-located with a renewable energy generator and an
inelastic load. Unlike many approaches in the literature, no distributional
assumptions are being made on the renewable energy generation or the real-time
prices. Building on the deep Q-networks algorithm, a reinforcement learning
approach utilizing a neural network is devised where the storage unit
operational constraints are respected. The neural network approximates the
action-value function which dictates what action (charging, discharging, etc.)
to take. Simulations indicate that near-optimal performance can be attained
with the proposed learning-based control policy for the storage units.
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Authors: Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew Lungren, Daniel Rubin, Christopher Ré
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Labeling training datasets has become a key barrier to building medical
machine learning models. One strategy is to generate training labels
programmatically, for example by applying natural language processing pipelines
to text reports associated with imaging studies. We propose cross-modal data
programming, which generalizes this intuitive strategy in a
theoretically-grounded way that enables simpler, clinician-driven input,
reduces required labeling time, and improves with additional unlabeled data. In
this approach, clinicians generate training labels for models defined over a
target modality (e.g. images or time series) by writing rules over an auxiliary
modality (e.g. text reports). The resulting technical challenge consists of
estimating the accuracies and correlations of these rules; we extend a recent
unsupervised generative modeling technique to handle this cross-modal setting
in a provably consistent way. Across four applications in radiography, computed
tomography, and electroencephalography, and using only several hours of
clinician time, our approach matches or exceeds the efficacy of
physician-months of hand-labeling with statistical significance, demonstrating
a fundamentally faster and more flexible way of building machine learning
models in medicine.
Machine learning approaches in Detecting the Depression from Resting-state Electroencephalogram (EEG): A Review Study
Authors: Milena Cukic Radenkovic
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, EEG, Electroencephalogram (EEG), Management

In this paper, we aimed at reviewing several different approaches present
today in the search for more accurate diagnostic and treatment management in
mental healthcare. Our focus is on mood disorders, and in particular on the
major depressive disorder (MDD). We are reviewing and discussing findings based
on neuroimaging studies (MRI and fMRI) first to get the impression of the body
of knowledge about the anatomical and functional differences in depression.
Then, we are focusing on less expensive data-driven approach, applicable for
everyday clinical practice, in particular, those based on
electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we
are discussing a group of applications used for detecting of depression based
on the resting state EEG (detection studies) and interventional studies (using
stimulus in their protocols or aiming to predict the outcome of therapy). We
conclude with a discussion and review of guidelines to improve the reliability
of developed models that could serve improvement of diagnostic of depression in
psychiatry.
A method on selecting reliable samples based on fuzziness in positive and unlabeled learning
Authors: TingTing Li, Weiya Fan, YunSong Luo
Date: 2019-03-26 00:00:00
Tasks: valid

Traditional semi-supervised learning uses only labeled instances to train a
classifier and then this classifier is utilized to classify unlabeled
instances, while sometimes there are only positive instances which are elements
of the target concept are available in the labeled set. Our research in this
paper the design of learning algorithms from positive and unlabeled instances
only. Among all the semi-supervised positive and unlabeled learning methods, it
is a fundamental step to extract useful information from unlabeled instances.
In this paper, we design a novel framework to take advantage of valid
information in unlabeled instances. In essence, this framework mainly includes
that (1) selects reliable negative instances through the fuzziness of the
instances; (2) chooses new positive instances based on the fuzziness of the
instances to expand the initial positive set, and we named these new instances
as reliable positive instances; (3) uses data editing technique to filter out
noise points with high fuzziness. The effectiveness of the presented algorithm
is verified by comparative experiments on UCI dataset.
Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
Authors: Neil D. Lawrence
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning solutions, in particular those based on deep learning
methods, form an underpinning of the current revolution in "artificial
intelligence" that has dominated popular press headlines and is having a
significant influence on the wider tech agenda. Here we give an overview of the
3Ds of ML systems design: Data, Design and Deployment. By considering the 3Ds
we can move towards \emph{data first} design.
Eigenvalue and Generalized Eigenvalue Problems: Tutorial
Authors: Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Date: 2019-03-25 00:00:00
Tasks: BIG-bench Machine Learning, Matrix Factorization / Decomposition

This paper is a tutorial for eigenvalue and generalized eigenvalue problems. We first introduce eigenvalue problem, eigen-decomposition (spectral decomposition), and generalized eigenvalue problem. Then, we mention the optimization problems which yield to the eigenvalue and generalized eigenvalue problems. We also provide examples from machine learning, including principal component analysis, kernel supervised principal component analysis, and Fisher discriminant analysis, which result in eigenvalue and generalized eigenvalue problems. Finally, we introduce the solutions to both eigenvalue and generalized eigenvalue problems.