pwc-archive/papers-with-abstracts huggingface dataset:
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Authors: Shane Barratt, Mykel Kochenderfer, Stephen Boyd
Date: 2018-10-22 00:00:00
Tasks: Collision Avoidance, Position

Models for predicting aircraft motion are an important component of modern
aeronautical systems. These models help aircraft plan collision avoidance
maneuvers and help conduct offline performance and safety analyses. In this
article, we develop a method for learning a probabilistic generative model of
aircraft motion in terminal airspace, the controlled airspace surrounding a
given airport. The method fits the model based on a historical dataset of
radar-based position measurements of aircraft landings and takeoffs at that
airport. We find that the model generates realistic trajectories, provides
accurate predictions, and captures the statistical properties of aircraft
trajectories. Furthermore, the model trains quickly, is compact, and allows for
efficient real-time inference.
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
Authors: Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan
Date: 2018-10-22 00:00:00

Optimization is commonly employed to determine the content of web pages, such
as to maximize conversions on landing pages or click-through rates on search
engine result pages. Often the layout of these pages can be decoupled into
several separate decisions. For example, the composition of a landing page may
involve deciding which image to show, which wording to use, what color
background to display, etc. Such optimization is a combinatorial problem over
an exponentially large decision space. Randomized experiments do not scale well
to this setting, and therefore, in practice, one is typically limited to
optimizing a single aspect of a web page at a time. This represents a missed
opportunity in both the speed of experimentation and the exploitation of
possible interactions between layout decisions.
  Here we focus on multivariate optimization of interactive web pages. We
formulate an approach where the possible interactions between different
components of the page are modeled explicitly. We apply bandit methodology to
explore the layout space efficiently and use hill-climbing to select optimal
content in realtime. Our algorithm also extends to contextualization and
personalization of layout selection. Simulation results show the suitability of
our approach to large decision spaces with strong interactions between content.
We further apply our algorithm to optimize a message that promotes adoption of
an Amazon service. After only a single week of online optimization, we saw a
21% conversion increase compared to the median layout. Our technique is
currently being deployed to optimize content across several locations at
Amazon.com.
Introducing Curvature to the Label Space
Authors: Conor Sheehan, Ben Day, Pietro Liò
Date: 2018-10-22 00:00:00
Tasks: All, General Classification

One-hot encoding is a labelling system that embeds classes as standard basis
vectors in a label space. Despite seeing near-universal use in supervised
categorical classification tasks, the scheme is problematic in its geometric
implication that, as all classes are equally distant, all classes are equally
different. This is inconsistent with most, if not all, real-world tasks due to
the prevalence of ancestral and convergent relationships generating a varying
degree of morphological similarity across classes. We address this issue by
introducing curvature to the label-space using a metric tensor as a
self-regulating method that better represents these relationships as a bolt-on,
learning-algorithm agnostic solution. We propose both general constraints and
specific statistical parameterizations of the metric and identify a direction
for future research using autoencoder-based parameterizations.
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Authors: Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville
Date: 2018-10-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Constituency Grammar Induction, Inductive Bias, Language Modeling, Language Modelling

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.
A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery
Authors: Nathan Jacobs, Adam Kraft, Muhammad Usman Rafique, Ranti Dev Sharma
Date: 2018-10-22 00:00:00

We propose a neural network component, the regional aggregation layer, that
makes it possible to train a pixel-level density estimator using only
coarse-grained density aggregates, which reflect the number of objects in an
image region. Our approach is simple to use and does not require
domain-specific assumptions about the nature of the density function. We
evaluate our approach on several synthetic datasets. In addition, we use this
approach to learn to estimate high-resolution population and housing density
from satellite imagery. In all cases, we find that our approach results in
better density estimates than a commonly used baseline. We also show how our
housing density estimator can be used to classify buildings as residential or
non-residential.
Adversarial Risk Bounds via Function Transformation
Authors: Justin Khim, Po-Ling Loh
Date: 2018-10-22 00:00:00
Tasks: General Classification

We derive bounds for a notion of adversarial risk, designed to characterize
the robustness of linear and neural network classifiers to adversarial
perturbations. Specifically, we introduce a new class of function
transformations with the property that the risk of the transformed functions
upper-bounds the adversarial risk of the original functions. This reduces the
problem of deriving bounds on the adversarial risk to the problem of deriving
risk bounds using standard learning-theoretic techniques. We then derive bounds
on the Rademacher complexities of the transformed function classes, obtaining
error rates on the same order as the generalization error of the original
function classes. We also discuss extensions of our theory to multiclass
classification and regression. Finally, we provide two algorithms for
optimizing the adversarial risk bounds in the linear case, and discuss
connections to regularization and distributional robustness.
Automatically Detecting Self-Reported Birth Defect Outcomes on Twitter for Large-scale Epidemiological Research
Authors: Ari Z. Klein, Abeed Sarker, Davy Weissenbacher, Graciela Gonzalez-Hernandez
Date: 2018-10-22 00:00:00
Tasks: BIG-bench Machine Learning

In recent work, we identified and studied a small cohort of Twitter users
whose pregnancies with birth defect outcomes could be observed via their
publicly available tweets. Exploiting social media's large-scale potential to
complement the limited methods for studying birth defects, the leading cause of
infant mortality, depends on the further development of automatic methods. The
primary objective of this study was to take the first step towards scaling the
use of social media for observing pregnancies with birth defect outcomes,
namely, developing methods for automatically detecting tweets by users
reporting their birth defect outcomes. We annotated and pre-processed
approximately 23,000 tweets that mention birth defects in order to train and
evaluate supervised machine learning algorithms, including feature-engineered
and deep learning-based classifiers. We also experimented with various
under-sampling and over-sampling approaches to address the class imbalance. A
Support Vector Machine (SVM) classifier trained on the original, imbalanced
data set, with n-grams, word clusters, and structural features, achieved the
best baseline performance for the positive classes: an F1-score of 0.65 for the
"defect" class and 0.51 for the "possible defect" class. Our contributions
include (i) natural language processing (NLP) and supervised machine learning
methods for automatically detecting tweets by users reporting their birth
defect outcomes, (ii) a comparison of feature-engineered and deep
learning-based classifiers trained on imbalanced, under-sampled, and
over-sampled data, and (iii) an error analysis that could inform classification
improvements using our publicly available corpus. Future work will focus on
automating user-level analyses for cohort inclusion.
A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards
Authors: Nicolai Häni, Pravakar Roy, Volkan Isler
Date: 2018-10-22 00:00:00
Tasks: Deep Learning, Yield Mapping In Apple Orchards

We present new methods for apple detection and counting based on recent deep
learning approaches and compare them with state-of-the-art results based on
classical methods. Our goal is to quantify performance improvements by neural
network-based methods compared to methods based on classical approaches.
Additionally, we introduce a complete system for counting apples in an entire
row. This task is challenging as it requires tracking fruits in images from
both sides of the row. We evaluate the performances of three fruit detection
methods and two fruit counting methods on six datasets. Results indicate that
the classical detection approach still outperforms the deep learning based
methods in the majority of the datasets. For fruit counting though, the deep
learning based approach performs better for all of the datasets. Combining the
classical detection method together with the neural network based counting
approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.
Two view constraints on the epipoles from few correspondences
Authors: Yoni Kasten, Michael Werman
Date: 2018-10-22 00:00:00
Tasks: Vocal Bursts Valence Prediction

In general it requires at least 7 point correspondences to compute the
fundamental matrix between views. We use the cross ratio invariance between
corresponding epipolar lines, stemming from epipolar line homography, to derive
a simple formulation for the relationship between epipoles and corresponding
points. We show how it can be used to reduce the number of required points for
the epipolar geometry when some information about the epipoles is available and
demonstrate this with a buddy search app.
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function
Authors: Xiaobin Hu, Hongwei Li, Yu Zhao, Chao Dong, Bjoern H. Menze, Marie Piraud
Date: 2018-10-22 00:00:00
Tasks: Brain Tumor Segmentation, Tumor Segmentation

For many segmentation tasks, especially for the biomedical image, the
topological prior is vital information which is useful to exploit. The
containment/nesting is a typical inter-class geometric relationship. In the
MICCAI Brain tumor segmentation challenge, with its three hierarchically nested
classes 'whole tumor', 'tumor core', 'active tumor', the nested classes
relationship is introduced into the 3D-residual-Unet architecture. The network
comprises a context aggregation pathway and a localization pathway, which
encodes increasingly abstract representation of the input as going deeper into
the network, and then recombines these representations with shallower features
to precisely localize the interest domain via a localization path. The
nested-class-prior is combined by proposing the multi-class activation function
and its corresponding loss function. The model is trained on the training
dataset of Brats2018, and 20% of the dataset is regarded as the validation
dataset to determine parameters. When the parameters are fixed, we retrain the
model on the whole training dataset. The performance achieved on the validation
leaderboard is 86%, 77% and 72% Dice scores for the whole tumor, enhancing
tumor and tumor core classes without relying on ensembles or complicated
post-processing steps. Based on the same start-of-the-art network architecture,
the accuracy of nested-class (enhancing tumor) is reasonably improved from 69%
to 72% compared with the traditional Softmax-based method which blind to
topological prior.
Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features
Authors: Philipp Tschandl, Giuseppe Argenziano, Majid Razmara, Jordan Yap
Date: 2018-10-22 00:00:00
Tasks: Content-Based Image Retrieval, Diagnostic, General Classification, Image Retrieval, Retrieval

Background: Automated classification of medical images through neural
networks can reach high accuracy rates but lack interpretability.
  Objectives: To compare the diagnostic accuracy obtained by using content
based image retrieval (CBIR) to retrieve visually similar dermatoscopic images
with corresponding disease labels against predictions made by a neural network.
  Methods: A neural network was trained to predict disease classes on
dermatoscopic images from three retrospectively collected image datasets
containing 888, 2750 and 16691 images respectively. Diagnosis predictions were
made based on the most commonly occurring diagnosis in visually similar images,
or based on the top-1 class prediction of the softmax output from the network.
Outcome measures were area under the ROC curve for predicting a malignant
lesion (AUC), multiclass-accuracy and mean average precision (mAP), measured on
unseen test images of the corresponding dataset.
  Results: In all three datasets the skin cancer predictions from CBIR
(evaluating the 16 most similar images) showed AUC values similar to softmax
predictions (0.842, 0.806 and 0.852 versus 0.830, 0.810 and 0.847 respectively;
p-value>0.99 for all). Similarly, the multiclass-accuracy of CBIR was
comparable to softmax predictions. Networks trained for detecting only 3
classes performed better on a dataset with 8 classes when using CBIR as
compared to softmax predictions (mAP 0.184 vs. 0.368 and 0.198 vs. 0.403
respectively).
  Conclusions: Presenting visually similar images based on features from a
neural network shows comparable accuracy to the softmax probability-based
diagnoses of convolutional neural networks. CBIR may be more helpful than a
softmax classifier in improving diagnostic accuracy of clinicians in a routine
clinical setting.
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions
Authors: Nicola Milano, Stefano Nolfi
Date: 2018-10-22 00:00:00
Tasks: regression

We demonstrate how efficiency of Cartesian Genetic Programming method can be
scaled up through the preferential selection of phenotypically larger
solutions, i.e. through the preferential selection of larger solutions among
equally good solutions. The advantage of the preferential selection of larger
solutions is validated on the six, seven and eight-bit parity problems, on a
dynamically varying problem involving the classification of binary patterns,
and on the Paige regression problem. In all cases, the preferential selection
of larger solutions provides an advantage in term of the performance of the
evolved solutions and in term of speed, the number of evaluations required to
evolve optimal or high-quality solutions. The advantage provided by the
preferential selection of larger solutions can be further extended by
self-adapting the mutation rate through the one-fifth success rule. Finally,
for problems like the Paige regression in which neutrality plays a minor role,
the advantage of the preferential selection of larger solutions can be extended
by preferring larger solutions also among quasi-neutral alternative candidate
solutions, i.e. solutions achieving slightly different performance.
Single Image Haze Removal using a Generative Adversarial Network
Authors: Bharath Raj N., Venkateswaran N
Date: 2018-10-22 00:00:00
Tasks: Generative Adversarial Network, Image Dehazing, Single Image Haze Removal

Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network to directly remove haze from an image. We employ the usage of the Tiramisu model in place of the classic U-Net model as the generator owing to its higher parameter efficiency and performance. Moreover, a patch based discriminator was used to reduce artefacts in the output. To further improve the perceptual quality of the output, a hybrid weighted loss function was designed and used to train the model. Experiments on synthetic and real world hazy images demonstrates that our model performs competitively with the state of the art methods.
A jamming transition from under- to over-parametrization affects loss landscape and generalization
Authors: Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart
Date: 2018-10-22 00:00:00

We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss. In the whole over-parametrized regime, poor minima of the loss are not encountered during training since the number of constraints to satisfy is too small to hamper minimization. Our findings support a link between this transition and the generalization properties of the network: as we increase the number of parameters of a given model, starting from an under-parametrized network, we observe that the generalization error displays three phases: (i) initial decay, (ii) increase until the transition point --- where it displays a cusp --- and (iii) slow decay toward a constant for the rest of the over-parametrized regime. Thereby we identify the region where the classical phenomenon of over-fitting takes place, and the region where the model keeps improving, in line with previous empirical observations for modern neural networks.
A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors
Authors: Babak Barazandeh, Mohammadhussein Rafieisakhaei, Sunwook Kim, Zhenyu, Kong, Maury A. Nussbaum
Date: 2018-10-22 00:00:00
Tasks: Dictionary Learning, General Classification

Classification methods based on sparse estimation have drawn much attention
recently, due to their effectiveness in processing high-dimensional data such
as images. In this paper, a method to improve the performance of a sparse
representation classification (SRC) approach is proposed; it is then applied to
the problem of online process monitoring of human workers, specifically manual
material handling (MMH) operations monitored using wearable sensors (involving
111 sensor channels). Our proposed method optimizes the design matrix (aka
dictionary) in the linear model used for SRC, minimizing its ill-posedness to
achieve a sparse solution. This procedure is based on the idea of dictionary
learning (DL): we optimize the design matrix formed by training datasets to
minimize both redundancy and coherency as well as reducing the size of these
datasets. Use of such optimized training data can subsequently improve
classification accuracy and help decrease the computational time needed for the
SRC; it is thus more applicable for online process monitoring. Performance of
the proposed methodology is demonstrated using wearable sensor data obtained
from manual material handling experiments, and is found to be superior to those
of benchmark methods in terms of accuracy, while also requiring computational
time appropriate for MMH online monitoring.
Digital holographic particle volume reconstruction using a deep neural network
Authors: Tomoyoshi Shimobaba, Takayuki Takahashi, Yota Yamamoto, Yutaka Endo, Atsushi Shiraki, Takashi Nishitsuji, Naoto Hoshikawa, Takashi Kakue, Tomoyosh Ito
Date: 2018-10-21 00:00:00

This paper proposes a particle volume reconstruction directly from an in-line
hologram using a deep neural network. Digital holographic volume reconstruction
conventionally uses multiple diffraction calculations to obtain sectional
reconstructed images from an in-line hologram, followed by detection of the
lateral and axial positions, and the sizes of particles by using focus metrics.
However, the axial resolution is limited by the numerical aperture of the
optical system, and the processes are time-consuming. The method proposed here
can simultaneously detect the lateral and axial positions, and the particle
sizes via a deep neural network (DNN). We numerically investigated the
performance of the DNN in terms of the errors in the detected positions and
sizes. The calculation time is faster than conventional diffracted-based
approaches.
Mechanism Design for Social Good
Authors: Rediet Abebe, Kira Goldner
Date: 2018-10-21 00:00:00

Across various domains--such as health, education, and housing--improving
societal welfare involves allocating resources, setting policies, targeting
interventions, and regulating activities. These solutions have an immense
impact on the day-to-day lives of individuals, whether in the form of access to
quality healthcare, labor market outcomes, or how votes are accounted for in a
democratic society. Problems that can have an out-sized impact on individuals
whose opportunities have historically been limited often pose conceptual and
technical challenges, requiring insights from many disciplines. Conversely, the
lack of interdisciplinary approach can leave these urgent needs unaddressed and
can even exacerbate underlying socioeconomic inequalities. To realize the
opportunities in these domains, we need to correctly set objectives and reason
about human behavior and actions. Doing so requires a deep grounding in the
field of interest and collaboration with domain experts who understand the
societal implications and feasibility of proposed solutions. These insights can
play an instrumental role in proposing algorithmically-informed policies.
  In this article, we describe the Mechanism Design for Social Good (MD4SG)
research agenda, which involves using insights from algorithms, optimization,
and mechanism design to improve access to opportunity. The MD4SG research
community takes an interdisciplinary, multi-stakeholder approach to improve
societal welfare. We discuss three exciting research avenues within MD4SG
related to improving access to opportunity in the developing world, labor
markets and discrimination, and housing. For each of these, we showcase ongoing
work, underline new directions, and discuss potential for implementing existing
work in practice.