pwc-archive/papers-with-abstracts huggingface dataset:
Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization
Authors: Hao Yan, Kamran Paynabar, Massimo Pacella
Date: 2018-07-26 00:00:00
Tasks: regression

Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM)
and laser 3D scanners have facilitated the collection of massive point cloud
data, beneficial for process monitoring, control and optimization. However, due
to their high dimensionality and structure complexity, modeling and analysis of
point clouds are still a challenge. In this paper, we utilize multilinear
algebra techniques and propose a set of tensor regression approaches to model
the variational patterns of point clouds and to link them to process variables.
The performance of the proposed methods is evaluated through simulations and a
real case study of turning process optimization.
EBIC: an open source software for high-dimensional and big data biclustering analyses
Authors: Patryk Orzechowski, Jason H. Moore
Date: 2018-07-26 00:00:00
Tasks: GPU

Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding support for big data, making it possible to efficiently run large genomic data mining analyses. Additional enhancements include integration with R and Bioconductor and an option to remove influence of missing value on the final result. Results: EBIC was applied to datasets of different sizes, including a large DNA methylation dataset with 436,444 rows. For the largest dataset we observed over 6.6 fold speedup in computation time on a cluster of 8 GPUs compared to running the method on a single GPU. This proves high scalability of the algorithm. Availability: The latest version of EBIC could be downloaded from http://github.com/EpistasisLab/ebic . Installation and usage instructions are also available online.
Scaled Simplex Representation for Subspace Clustering
Authors: Jun Xu, Mengyang Yu, Ling Shao, WangMeng Zuo, Deyu Meng, Lei Zhang, David Zhang
Date: 2018-07-26 00:00:00
Tasks: Clustering

The self-expressive property of data points, i.e., each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspace clustering methods. Most self-expressive methods usually construct a feasible affinity matrix from a coefficient matrix, obtained by solving an optimization problem. However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations. This consequently damages the inherent correlations among the data. Besides, the affine constraint used in these methods is not flexible enough for practical applications. To overcome these problems, in this paper, we introduce a scaled simplex representation (SSR) for subspace clustering problem. Specifically, the non-negative constraint is used to make the coefficient matrix physically meaningful, and the coefficient vector is constrained to be summed up to a scalar s<1 to make it more discriminative. The proposed SSR based subspace clustering (SSRSC) model is reformulated as a linear equality-constrained problem, which is solved efficiently under the alternating direction method of multipliers framework. Experiments on benchmark datasets demonstrate that the proposed SSRSC algorithm is very efficient and outperforms state-of-the-art subspace clustering methods on accuracy. The code can be found at https://github.com/csjunxu/SSRSC.
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
Authors: Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, Xinge You
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Fine-Grained Visual Recognition

Fine-grained visual recognition is challenging because it highly relies on
the modeling of various semantic parts and fine-grained feature learning.
Bilinear pooling based models have been shown to be effective at fine-grained
recognition, while most previous approaches neglect the fact that inter-layer
part feature interaction and fine-grained feature learning are mutually
correlated and can reinforce each other. In this paper, we present a novel
model to address these issues. First, a cross-layer bilinear pooling approach
is proposed to capture the inter-layer part feature relations, which results in
superior performance compared with other bilinear pooling based approaches.
Second, we propose a novel hierarchical bilinear pooling framework to integrate
multiple cross-layer bilinear features to enhance their representation
capability. Our formulation is intuitive, efficient and achieves
state-of-the-art results on the widely used fine-grained recognition datasets.
A Data-Efficient Approach to Precise and Controlled Pushing
Authors: Maria Bauza, Francois R. Hogan, Alberto Rodriguez
Date: 2018-07-26 00:00:00
Tasks: Friction, Model Predictive Control

Decades of research in control theory have shown that simple controllers,
when provided with timely feedback, can control complex systems. Pushing is an
example of a complex mechanical system that is difficult to model accurately
due to unknown system parameters such as coefficients of friction and pressure
distributions. In this paper, we explore the data-complexity required for
controlling, rather than modeling, such a system. Results show that a
model-based control approach, where the dynamical model is learned from data,
is capable of performing complex pushing trajectories with a minimal amount of
training data (10 data points). The dynamics of pushing interactions are
modeled using a Gaussian process (GP) and are leveraged within a model
predictive control approach that linearizes the GP and imposes actuator and
task constraints for a planar manipulation task.
Neural State Classification for Hybrid Systems
Authors: Dung Phan, Nicola Paoletti, Timothy Zhang, Radu Grosu, Scott A. Smolka, Scott D. Stoller
Date: 2018-07-26 00:00:00
Tasks: Classification, General Classification

We introduce the State Classification Problem (SCP) for hybrid systems, and
present Neural State Classification (NSC) as an efficient solution technique.
SCP generalizes the model checking problem as it entails classifying each state
$s$ of a hybrid automaton as either positive or negative, depending on whether
or not $s$ satisfies a given time-bounded reachability specification. This is
an interesting problem in its own right, which NSC solves using
machine-learning techniques, Deep Neural Networks in particular. State
classifiers produced by NSC tend to be very efficient (run in constant time and
space), but may be subject to classification errors. To quantify and mitigate
such errors, our approach comprises: i) techniques for certifying, with
statistical guarantees, that an NSC classifier meets given accuracy levels; ii)
tuning techniques, including a novel technique based on adversarial sampling,
that can virtually eliminate false negatives (positive states classified as
negative), thereby making the classifier more conservative. We have applied NSC
to six nonlinear hybrid system benchmarks, achieving an accuracy of 99.25% to
99.98%, and a false-negative rate of 0.0033 to 0, which we further reduced to
0.0015 to 0 after tuning the classifier. We believe that this level of accuracy
is acceptable in many practical applications, and that these results
demonstrate the promise of the NSC approach.
End-to-End Learning via a Convolutional Neural Network for Cancer Cell Line Classification
Authors: Darlington Ahiale Akogo, Xavier-Lewis Palmer
Date: 2018-07-25 00:00:00
Tasks: General Classification

Computer Vision for automated analysis of cells and tissues usually include
extracting features from images before analyzing such features via various
Machine Learning and Machine Vision algorithms. We developed a Convolutional
Neural Network model that classifies MDA-MB-468 and MCF7 breast cancer cells
via brightfield microscopy images without the need of any prior feature
extraction. Our 6-layer Convolutional Neural Network is directly trained,
validated and tested on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell
line in an end-to-end fashion, allowing a system to distinguish between
different cancer cell types. The model takes in as input imaged breast cancer
cell line and then outputs the cell line type (MDA-MB-468 or MCF7) as predicted
probabilities between the two classes. Our model scored a 99% Accuracy.
Evaluating Creativity in Computational Co-Creative Systems
Authors: Pegah Karimi, Kazjon Grace, Mary Lou Maher, Nicholas Davis
Date: 2018-07-25 00:00:00

This paper provides a framework for evaluating creativity in co-creative
systems: those that involve computer programs collaborating with human users on
creative tasks. We situate co-creative systems within a broader context of
computational creativity and explain the unique qualities of these systems. We
present four main questions that can guide evaluation in co-creative systems:
Who is evaluating the creativity, what is being evaluated, when does evaluation
occur and how the evaluation is performed. These questions provide a framework
for comparing how existing co-creative systems evaluate creativity, and we
apply them to examples of co-creative systems in art, humor, games and
robotics. We conclude that existing co-creative systems tend to focus on
evaluating the user experience. Adopting evaluation methods from autonomous
creative systems may lead to co-creative systems that are self-aware and
intentional.
Persuasive Faces: Generating Faces in Advertisements
Authors: Christopher Thomas, Adriana Kovashka
Date: 2018-07-25 00:00:00
Tasks: Face Generation, Generative Adversarial Network

In this paper, we examine the visual variability of objects across different
ad categories, i.e. what causes an advertisement to be visually persuasive. We
focus on modeling and generating faces which appear to come from different
types of ads. For example, if faces in beauty ads tend to be women wearing
lipstick, a generative model should portray this distinct visual appearance.
Training generative models which capture such category-specific differences is
challenging because of the highly diverse appearance of faces in ads and the
relatively limited amount of available training data. To address these
problems, we propose a conditional variational autoencoder which makes use of
predicted semantic attributes and facial expressions as a supervisory signal
when training. We show how our model can be used to produce visually distinct
faces which appear to be from a fixed ad topic category. Our human studies and
quantitative and qualitative experiments confirm that our method greatly
outperforms a variety of baselines, including two variations of a
state-of-the-art generative adversarial network, for transforming faces to be
more ad-category appropriate. Finally, we show preliminary generation results
for other types of objects, conditioned on an ad topic.
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
Authors: Caio Corro, Ivan Titov
Date: 2018-07-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: Dependency Parsing, Word Embeddings

Human annotation for syntactic parsing is expensive, and large resources are
available only for a fraction of languages. A question we ask is whether one
can leverage abundant unlabeled texts to improve syntactic parsers, beyond just
using the texts to obtain more generalisable lexical features (i.e. beyond word
embeddings). To this end, we propose a novel latent-variable generative model
for semi-supervised syntactic dependency parsing. As exact inference is
intractable, we introduce a differentiable relaxation to obtain approximate
samples and compute gradients with respect to the parser parameters. Our method
(Differentiable Perturb-and-Parse) relies on differentiable dynamic programming
over stochastically perturbed edge scores. We demonstrate effectiveness of our
approach with experiments on English, French and Swedish.
Do Better ImageNet Models Transfer Better... for Image Recommendation?
Authors: Felipe del Rio, Pablo Messina, Vicente Dominguez, Denis Parra
Date: 2018-07-25 00:00:00
Tasks: Recommendation Systems, Transfer Learning

Visual embeddings from Convolutional Neural Networks (CNN) trained on the
ImageNet dataset for the ILSVRC challenge have shown consistently good
performance for transfer learning and are widely used in several tasks,
including image recommendation. However, some important questions have not yet
been answered in order to use these embeddings for a larger scope of
recommendation domains: a) Do CNNs that perform better in ImageNet are also
better for transfer learning in content-based image recommendation?, b) Does
fine-tuning help to improve performance? and c) Which is the best way to
perform the fine-tuning?
  In this paper we compare several CNN models pre-trained with ImageNet to
evaluate their transfer learning performance to an artwork image recommendation
task. Our results indicate that models with better performance in the ImageNet
challenge do not always imply better transfer learning for recommendation tasks
(e.g. NASNet vs. ResNet). Our results also show that fine-tuning can be helpful
even with a small dataset, but not every fine-tuning works. Our results can
inform other researchers and practitioners on how to train their CNNs for
better transfer learning towards image recommendation systems.
Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data
Authors: Samuel J. Weisenthal, Caroline Quill, Samir Farooq, Henry Kautz, Martin S. Zand
Date: 2018-07-25 00:00:00
Tasks: Kidney Function

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated
with increased mortality, morbidity, length of stay, and hospital cost. Since
AKI is sometimes preventable, there is great interest in prediction. Most
existing studies consider all patients and therefore restrict to features
available in the first hours of hospitalization. Here, the focus is instead on
rehospitalized patients, a cohort in which rich longitudinal features from
prior hospitalizations can be analyzed. Our objective is to provide a risk
score directly at hospital re-entry. Gradient boosting, penalized logistic
regression (with and without stability selection), and a recurrent neural
network are trained on two years of adult inpatient EHR data (3,387 attributes
for 34,505 patients who generated 90,013 training samples with 5,618 cases and
84,395 controls). Predictions are internally evaluated with 50 iterations of
5-fold grouped cross-validation with special emphasis on calibration, an
analysis of which is performed at the patient as well as hospitalization level.
Error is assessed with respect to diagnosis, race, age, gender, AKI
identification method, and hospital utilization. In an additional experiment,
the regularization penalty is severely increased to induce parsimony and
interpretability. Predictors identified for rehospitalized patients are also
reported with a special analysis of medications that might be modifiable risk
factors. Insights from this study might be used to construct a predictive tool
for AKI in rehospitalized patients. An accurate estimate of AKI risk at
hospital entry might serve as a prior for an admitting provider or another
predictive algorithm.
Where are the Blobs: Counting by Localization with Point Supervision
Authors: Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, Object Counting, regression

Object counting is an important task in computer vision due to its growing
demand in applications such as surveillance, traffic monitoring, and counting
everyday objects. State-of-the-art methods use regression-based optimization
where they explicitly learn to count the objects of interest. These often
perform better than detection-based methods that need to learn the more
difficult task of predicting the location, size, and shape of each object.
However, we propose a detection-based method that does not need to estimate the
size and shape of the objects and that outperforms regression-based methods.
Our contributions are three-fold: (1) we propose a novel loss function that
encourages the network to output a single blob per object instance using
point-level annotations only; (2) we design two methods for splitting large
predicted blobs between object instances; and (3) we show that our method
achieves new state-of-the-art results on several challenging datasets including
the Pascal VOC and the Penguins dataset. Our method even outperforms those that
use stronger supervision such as depth features, multi-point annotations, and
bounding-box labels.
Local Orthogonal-Group Testing
Authors: Ahmet Iscen, Ondrej Chum
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Retrieval, Retrieval

This work addresses approximate nearest neighbor search applied in the domain
of large-scale image retrieval. Within the group testing framework we propose
an efficient off-line construction of the search structures. The linear-time
complexity orthogonal grouping increases the probability that at most one
element from each group is matching to a given query. Non-maxima suppression
with each group efficiently reduces the number of false positive results at no
extra cost. Unlike in other well-performing approaches, all processing is
local, fast, and suitable to process data in batches and in parallel. We
experimentally show that the proposed method achieves search accuracy of the
exhaustive search with significant reduction in the search complexity. The
method can be naturally combined with existing embedding methods.
Robustness to fundamental uncertainty in AGI alignment
Authors: G Gordon Worley III
Date: 2018-07-25 00:00:00
Tasks: Clustering

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of philosophical and practical uncertainty associated with the alignment problem by limiting and choosing necessary assumptions to reduce the risk of false positives. Herein we explore in detail two relevant points of uncertainty that AGI alignment research hinges on---meta-ethical uncertainty and uncertainty about mental phenomena---and show how to reduce false positives in response to them.
ADVIO: An authentic dataset for visual-inertial odometry
Authors: Santiago Cortés, Arno Solin, Esa Rahtu, Juho Kannala
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Benchmarking

The lack of realistic and open benchmarking datasets for pedestrian
visual-inertial odometry has made it hard to pinpoint differences in published
methods. Existing datasets either lack a full six degree-of-freedom
ground-truth or are limited to small spaces with optical tracking systems. We
take advantage of advances in pure inertial navigation, and develop a set of
versatile and challenging real-world computer vision benchmark sets for
visual-inertial odometry. For this purpose, we have built a test rig equipped
with an iPhone, a Google Pixel Android phone, and a Google Tango device. We
provide a wide range of raw sensor data that is accessible on almost any
modern-day smartphone together with a high-quality ground-truth track. We also
compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple
ARKit with two recent methods published in academic forums. The data sets cover
both indoor and outdoor cases, with stairs, escalators, elevators, office
environments, a shopping mall, and metro station.
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework
Authors: Simon Bussy, Raphaël Veil, Vincent Looten, Anita Burgun, Stéphane Gaïffas, Agathe Guilloux, Brigitte Ranque, Anne-Sophie Jannot
Date: 2018-07-25 00:00:00
Tasks: Prediction, Prognosis, Readmission Prediction, Survival Analysis, Variable Selection

Background: Choosing the most performing method in terms of outcome
prediction or variables selection is a recurring problem in prognosis studies,
leading to many publications on methods comparison. But some aspects have
received little attention. First, most comparison studies treat prediction
performance and variable selection aspects separately. Second, methods are
either compared within a binary outcome setting (based on an arbitrarily chosen
delay) or within a survival setting, but not both. In this paper, we propose a
comparison methodology to weight up those different settings both in terms of
prediction and variables selection, while incorporating advanced machine
learning strategies. Methods: Using a high-dimensional case study on a
sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the
binary outcome setting, we consider logistic regression (LR), support vector
machine (SVM), random forest (RF), gradient boosting (GB) and neural network
(NN); while on the survival analysis setting, we consider the Cox Proportional
Hazards (PH), the CURE and the C-mix models. We then compare performances of
all methods both in terms of risk prediction and variable selection, with a
focus on the use of Elastic-Net regularization technique. Results: Among all
assessed statistical methods assessed, the C-mix model yields the better
performances in both the two considered settings, as well as interesting
interpretation aspects. There is some consistency in selected covariates across
methods within a setting, but not much across the two settings. Conclusions: It
appears that learning withing the survival setting first, and then going back
to a binary prediction using the survival estimates significantly enhance
binary predictions.
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics
Authors: Simon Bussy, Mokhtar Z. Alaya, Anne-Sophie Jannot, Agathe Guilloux
Date: 2018-07-25 00:00:00
Tasks: feature selection, Survival Analysis

We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer datasets. On these high-dimensional datasets, our proposed method significantly outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.