pwc-archive/papers-with-abstracts huggingface dataset:
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Authors: Bryan Wilder, Bistra Dilkina, Milind Tambe
Date: 2018-09-14 00:00:00
Tasks: Combinatorial Optimization

Creating impact in real-world settings requires artificial intelligence
techniques to span the full pipeline from data, to predictive models, to
decisions. These components are typically approached separately: a machine
learning model is first trained via a measure of predictive accuracy, and then
its predictions are used as input into an optimization algorithm which produces
a decision. However, the loss function used to train the model may easily be
misaligned with the end goal, which is to make the best decisions possible.
Hand-tuning the loss function to align with optimization is a difficult and
error-prone process (which is often skipped entirely).
  We focus on combinatorial optimization problems and introduce a general
framework for decision-focused learning, where the machine learning model is
directly trained in conjunction with the optimization algorithm to produce
high-quality decisions. Technically, our contribution is a means of integrating
common classes of discrete optimization problems into deep learning or other
predictive models, which are typically trained via gradient descent. The main
idea is to use a continuous relaxation of the discrete problem to propagate
gradients through the optimization procedure. We instantiate this framework for
two broad classes of combinatorial problems: linear programs and submodular
maximization. Experimental results across a variety of domains show that
decision-focused learning often leads to improved optimization performance
compared to traditional methods. We find that standard measures of accuracy are
not a reliable proxy for a predictive model's utility in optimization, and our
method's ability to specify the true goal as the model's training objective
yields substantial dividends across a range of decision problems.
Mugeetion: Musical Interface Using Facial Gesture and Emotion
Authors: Eunjeong Stella Koh, Shahrokh Yadegari
Date: 2018-09-14 00:00:00

People feel emotions when listening to music. However, emotions are not
tangible objects that can be exploited in the music composition process as they
are difficult to capture and quantify in algorithms. We present a novel musical
interface, Mugeetion, designed to capture occurring instances of emotional
states from users' facial gestures and relay that data to associated musical
features. Mugeetion can translate qualitative data of emotional states into
quantitative data, which can be utilized in the sound generation process. We
also presented and tested this work in the exhibition of sound installation,
Hearing Seascape, using the audiences' facial expressions. Audiences heard
changes in the background sound based on their emotional state. The process
contributes multiple research areas, such as gesture tracking systems,
emotion-sound modeling, and the connection between sound and facial gesture.
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
Authors: Uwe Springmann, Christian Reul, Stefanie Dipper, Johannes Baiter
Date: 2018-09-14 00:00:00
Tasks: Optical Character Recognition (OCR)

In this paper we describe a dataset of German and Latin \textit{ground truth}
(GT) for historical OCR in the form of printed text line images paired with
their transcription. This dataset, called \textit{GT4HistOCR}, consists of
313,173 line pairs covering a wide period of printing dates from incunabula
from the 15th century to 19th century books printed in Fraktur types and is
openly available under a CC-BY 4.0 license. The special form of GT as line
image/transcription pairs makes it directly usable to train state-of-the-art
recognition models for OCR software employing recurring neural networks in LSTM
architecture such as Tesseract 4 or OCRopus. We also provide some pretrained
OCRopus models for subcorpora of our dataset yielding between 95\% (early
printings) and 98\% (19th century Fraktur printings) character accuracy rates
on unseen test cases, a Perl script to harmonize GT produced by different
transcription rules, and give hints on how to construct GT for OCR purposes
which has requirements that may differ from linguistically motivated
transcriptions.
Elastic Registration of Geodesic Vascular Graphs
Authors: Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Date: 2018-09-14 00:00:00
Tasks: Graph Matching

Vascular graphs can embed a number of high-level features, from morphological
parameters, to functional biomarkers, and represent an invaluable tool for
longitudinal and cross-sectional clinical inference. This, however, is only
feasible when graphs are co-registered together, allowing coherent multiple
comparisons. The robust registration of vascular topologies stands therefore as
key enabling technology for group-wise analyses. In this work, we present an
end-to-end vascular graph registration approach, that aligns networks with
non-linear geometries and topological deformations, by introducing a novel
overconnected geodesic vascular graph formulation, and without enforcing any
anatomical prior constraint. The 3D elastic graph registration is then
performed with state-of-the-art graph matching methods used in computer vision.
Promising results of vascular matching are found using graphs from synthetic
and real angiographies. Observations and future designs are discussed towards
potential clinical applications.
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Authors: Luis M. Vaquero, Felix Cuadrado
Date: 2018-09-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads.
MoSculp: Interactive Visualization of Shape and Time
Authors: Xiuming Zhang, Tali Dekel, Tianfan Xue, Andrew Owens, Qiurui He, Jiajun Wu, Stefanie Mueller, William T. Freeman
Date: 2018-09-14 00:00:00
Tasks: 3D geometry

We present a system that allows users to visualize complex human motion via
3D motion sculptures---a representation that conveys the 3D structure swept by
a human body as it moves through space. Given an input video, our system
computes the motion sculptures and provides a user interface for rendering it
in different styles, including the options to insert the sculpture back into
the original video, render it in a synthetic scene or physically print it.
  To provide this end-to-end workflow, we introduce an algorithm that estimates
that human's 3D geometry over time from a set of 2D images and develop a
3D-aware image-based rendering approach that embeds the sculpture back into the
scene. By automating the process, our system takes motion sculpture creation
out of the realm of professional artists, and makes it applicable to a wide
range of existing video material.
  By providing viewers with 3D information, motion sculptures reveal space-time
motion information that is difficult to perceive with the naked eye, and allow
viewers to interpret how different parts of the object interact over time. We
validate the effectiveness of this approach with user studies, finding that our
motion sculpture visualizations are significantly more informative about motion
than existing stroboscopic and space-time visualization methods.
Blameworthiness in Strategic Games
Authors: Pavel Naumov, Jia Tao
Date: 2018-09-14 00:00:00

There are multiple notions of coalitional responsibility. The focus of this
paper is on the blameworthiness defined through the principle of alternative
possibilities: a coalition is blamable for a statement if the statement is
true, but the coalition had a strategy to prevent it. The main technical result
is a sound and complete bimodal logical system that describes properties of
blameworthiness in one-shot games.
A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation
Authors: Leonardo Gabrielli, Stefano Tomassetti, Stefano Squartini, Carlo Zinato, Stefano Guaiana
Date: 2018-09-14 00:00:00
Tasks: Deep Learning, parameter estimation, Stochastic Optimization

One of the challenges in computational acoustics is the identification of
models that can simulate and predict the physical behavior of a system
generating an acoustic signal. Whenever such models are used for commercial
applications an additional constraint is the time-to-market, making automation
of the sound design process desirable. In previous works, a computational sound
design approach has been proposed for the parameter estimation problem
involving timbre matching by deep learning, which was applied to the synthesis
of pipe organ tones. In this work we refine previous results by introducing the
former approach in a multi-stage algorithm that also adds heuristics and a
stochastic optimization method operating on objective cost functions based on
psychoacoustics. The optimization method shows to be able to refine the first
estimate given by the deep learning approach and substantially improve the
objective metrics, with the additional benefit of reducing the sound design
process time. Subjective listening tests are also conducted to gather
additional insights on the results.
Hardware-Aware Machine Learning: Modeling and Optimization
Authors: Diana Marculescu, Dimitrios Stamoulis, Ermao Cai
Date: 2018-09-14 00:00:00
Tasks: BIG-bench Machine Learning

Recent breakthroughs in Deep Learning (DL) applications have made DL models a
key component in almost every modern computing system. The increased popularity
of DL applications deployed on a wide-spectrum of platforms have resulted in a
plethora of design challenges related to the constraints introduced by the
hardware itself. What is the latency or energy cost for an inference made by a
Deep Neural Network (DNN)? Is it possible to predict this latency or energy
consumption before a model is trained? If yes, how can machine learners take
advantage of these models to design the hardware-optimal DNN for deployment?
From lengthening battery life of mobile devices to reducing the runtime
requirements of DL models executing in the cloud, the answers to these
questions have drawn significant attention.
  One cannot optimize what isn't properly modeled. Therefore, it is important
to understand the hardware efficiency of DL models during serving for making an
inference, before even training the model. This key observation has motivated
the use of predictive models to capture the hardware performance or energy
efficiency of DL applications. Furthermore, DL practitioners are challenged
with the task of designing the DNN model, i.e., of tuning the hyper-parameters
of the DNN architecture, while optimizing for both accuracy of the DL model and
its hardware efficiency. Therefore, state-of-the-art methodologies have
proposed hardware-aware hyper-parameter optimization techniques. In this paper,
we provide a comprehensive assessment of state-of-the-art work and selected
results on the hardware-aware modeling and optimization for DL applications. We
also highlight several open questions that are poised to give rise to novel
hardware-aware designs in the next few years, as DL applications continue to
significantly impact associated hardware systems and platforms.
Real Time System for Facial Analysis
Authors: Janne Tommola, Pedram Ghazi, Bishwo Adhikari, Heikki Huttunen
Date: 2018-09-14 00:00:00
Tasks: Anatomy, Face Detection

In this paper we describe the anatomy of a real-time facial analysis system.
The system recognizes the age, gender and facial expression from users in
appearing in front of the camera. All components are based on convolutional
neural networks, whose accuracy we study on commonly used training and
evaluation sets. A key contribution of the work is the description of the
interplay between processing threads for frame grabbing, face detection and the
three types of recognition. The python code for executing the system uses
common libraries--keras/tensorflow, opencv and dlib--and is available for
download.
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
Authors: Panagiotis Mandros, Mario Boley, Jilles Vreeken
Date: 2018-09-14 00:00:00
Tasks: Heuristic Search

The reliable fraction of information is an attractive score for quantifying
(functional) dependencies in high-dimensional data. In this paper, we
systematically explore the algorithmic implications of using this measure for
optimization. We show that the problem is NP-hard, which justifies the usage of
worst-case exponential-time as well as heuristic search methods. We then
substantially improve the practical performance for both optimization styles by
deriving a novel admissible bounding function that has an unbounded potential
for additional pruning over the previously proposed one. Finally, we
empirically investigate the approximation ratio of the greedy algorithm and
show that it produces highly competitive results in a fraction of time needed
for complete branch-and-bound style search.
Are screening methods useful in feature selection? An empirical study
Authors: Mingyuan Wang, Adrian Barbu
Date: 2018-09-14 00:00:00
Tasks: Classification, feature selection, General Classification, regression

Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression model. While there are many such filter methods, there is a need for an objective evaluation of these methods. Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them. For this purpose, many popular screening methods are partnered in this paper with three regression learners and five classification learners and evaluated on ten real datasets to obtain accuracy criteria such as R-square and area under the ROC curve (AUC). The obtained results are compared through curve plots and comparison tables in order to find out whether screening methods help improve the performance of learning algorithms and how they fare with each other. Our findings revealed that the screening methods were useful in improving the prediction of the best learner on two regression and two classification datasets out of the ten datasets evaluated.
Socially Aware Kalman Neural Networks for Trajectory Prediction
Authors: Ce Ju, Zheng Wang, Xiao-Yu Zhang
Date: 2018-09-14 00:00:00
Tasks: Autonomous Vehicles, Prediction, Trajectory Prediction

Trajectory prediction is a critical technique in the navigation of robots and autonomous vehicles. However, the complex traffic and dynamic uncertainties yield challenges in the effectiveness and robustness in modeling. We purpose a data-driven approach socially aware Kalman neural networks (SAKNN) where the interaction layer and the Kalman layer are embedded in the architecture, resulting in a class of architectures with huge potential to directly learn from high variance sensor input and robustly generate low variance outcomes. The evaluation of our approach on NGSIM dataset demonstrates that SAKNN performs state-of-the-art on prediction effectiveness in a relatively long-term horizon and significantly improves the signal-to-noise ratio of the predicted signal.
SCORES: Shape Composition with Recursive Substructure Priors
Authors: Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Renjiao Yi, Hao Zhang
Date: 2018-09-14 00:00:00

We introduce SCORES, a recursive neural network for shape composition. Our
network takes as input sets of parts from two or more source 3D shapes and a
rough initial placement of the parts. It outputs an optimized part structure
for the composed shape, leading to high-quality geometry construction. A unique
feature of our composition network is that it is not merely learning how to
connect parts. Our goal is to produce a coherent and plausible 3D shape,
despite large incompatibilities among the input parts. The network may
significantly alter the geometry and structure of the input parts and
synthesize a novel shape structure based on the inputs, while adding or
removing parts to minimize a structure plausibility loss. We design SCORES as a
recursive autoencoder network. During encoding, the input parts are recursively
grouped to generate a root code. During synthesis, the root code is decoded,
recursively, to produce a new, coherent part assembly. Assembled shape
structures may be novel, with little global resemblance to training exemplars,
yet have plausible substructures. SCORES therefore learns a hierarchical
substructure shape prior based on per-node losses. It is trained on structured
shapes from ShapeNet, and is applied iteratively to reduce the plausibility
loss.We showresults of shape composition from multiple sources over different
categories of man-made shapes and compare with state-of-the-art alternatives,
demonstrating that our network can significantly expand the range of composable
shapes for assembly-based modeling.
Style Augmentation: Data Augmentation via Style Randomization
Authors: Philip T. Jackson, Amir Atapour-Abarghouei, Stephen Bonner, Toby Breckon, Boguslaw Obara
Date: 2018-09-14 00:00:00
Tasks: Classification, Data Augmentation, Depth Estimation, Domain Adaptation, General Classification, Monocular Depth Estimation, Style Transfer

We introduce style augmentation, a new form of data augmentation based on
random style transfer, for improving the robustness of convolutional neural
networks (CNN) over both classification and regression based tasks. During
training, our style augmentation randomizes texture, contrast and color, while
preserving shape and semantic content. This is accomplished by adapting an
arbitrary style transfer network to perform style randomization, by sampling
input style embeddings from a multivariate normal distribution instead of
inferring them from a style image. In addition to standard classification
experiments, we investigate the effect of style augmentation (and data
augmentation generally) on domain transfer tasks. We find that data
augmentation significantly improves robustness to domain shift, and can be used
as a simple, domain agnostic alternative to domain adaptation. Comparing style
augmentation against a mix of seven traditional augmentation techniques, we
find that it can be readily combined with them to improve network performance.
We validate the efficacy of our technique with domain transfer experiments in
classification and monocular depth estimation, illustrating consistent
improvements in generalization.
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Authors: Lasse Hansen, Jasper Diesel, Mattias P. Heinrich
Date: 2018-09-14 00:00:00

Graph convolutional networks are a new promising learning approach to deal
with data on irregular domains. They are predestined to overcome certain
limitations of conventional grid-based architectures and will enable efficient
handling of point clouds or related graphical data representations, e.g.
superpixel graphs. Learning feature extractors and classifiers on 3D point
clouds is still an underdeveloped area and has potential restrictions to equal
graph topologies. In this work, we derive a new architectural design that
combines rotationally and topologically invariant graph diffusion operators and
node-wise feature learning through 1x1 convolutions. By combining multiple
isotropic diffusion operations based on the Laplace-Beltrami operator, we can
learn an optimal linear combination of diffusion kernels for effective feature
propagation across nodes on an irregular graph. We validated our approach for
learning point descriptors as well as semantic classification on real 3D point
clouds of human poses and demonstrate an improvement from 85% to 95% in Dice
overlap with our multi-kernel approach.
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Authors: Chung-Chi Chen, Hen-Hsen Huang, Yow-Ting Shiue, Hsin-Hsi Chen
Date: 2018-09-14 00:00:00
Tasks: Decision Making, Financial Analysis, General Classification

Numerals that contain much information in financial documents are crucial for
financial decision making. They play different roles in financial analysis
processes. This paper is aimed at understanding the meanings of numerals in
financial tweets for fine-grained crowd-based forecasting. We propose a
taxonomy that classifies the numerals in financial tweets into 7 categories,
and further extend some of these categories into several subcategories. Neural
network-based models with word and character-level encoders are proposed for
7-way classification and 17-way classification. We perform backtest to confirm
the effectiveness of the numeric opinions made by the crowd. This work is the
first attempt to understand numerals in financial social media data, and we
provide the first comparison of fine-grained opinion of individual investors
and analysts based on their forecast price. The numeral corpus used in our
experiments, called FinNum 1.0 , is available for research purposes.
Adaptive Sampling Towards Fast Graph Representation Learning
Authors: Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang
Date: 2018-09-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Graph Representation Learning, Node Classification, Representation Learning

Graph Convolutional Networks (GCNs) have become a crucial tool on learning
representations of graph vertices. The main challenge of adapting GCNs on
large-scale graphs is the scalability issue that it incurs heavy cost both in
computation and memory due to the uncontrollable neighborhood expansion across
layers. In this paper, we accelerate the training of GCNs through developing an
adaptive layer-wise sampling method. By constructing the network layer by layer
in a top-down passway, we sample the lower layer conditioned on the top one,
where the sampled neighborhoods are shared by different parent nodes and the
over expansion is avoided owing to the fixed-size sampling. More importantly,
the proposed sampler is adaptive and applicable for explicit variance
reduction, which in turn enhances the training of our method. Furthermore, we
propose a novel and economical approach to promote the message passing over
distant nodes by applying skip connections. Intensive experiments on several
benchmarks verify the effectiveness of our method regarding the classification
accuracy while enjoying faster convergence speed.
Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems
Authors: Vaishak Belle, Hector J. Levesque
Date: 2018-09-14 00:00:00

Among the many approaches for reasoning about degrees of belief in the
presence of noisy sensing and acting, the logical account proposed by Bacchus,
Halpern, and Levesque is perhaps the most expressive. While their formalism is
quite general, it is restricted to fluents whose values are drawn from discrete
finite domains, as opposed to the continuous domains seen in many robotic
applications. In this work, we show how this limitation in that approach can be
lifted. By dealing seamlessly with both discrete distributions and continuous
densities within a rich theory of action, we provide a very general logical
specification of how belief should change after acting and sensing in complex
noisy domains.