pwc-archive/papers-with-abstracts huggingface dataset:
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis
Authors: Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, Jungong Han
Date: 2017-05-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, Object Recognition, Zero-Shot Learning

Robust object recognition systems usually rely on powerful feature extraction
mechanisms from a large number of real images. However, in many realistic
applications, collecting sufficient images for ever-growing new classes is
unattainable. In this paper, we propose a new Zero-shot learning (ZSL)
framework that can synthesise visual features for unseen classes without
acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS)
algorithm, semantic attributes are effectively utilised as an intermediate clue
to synthesise unseen visual features at the training stage. Hereafter, ZSL
recognition is converted into the conventional supervised problem, i.e. the
synthesised visual features can be straightforwardly fed to typical classifiers
such as SVM. On four benchmark datasets, we demonstrate the benefit of using
synthesised unseen data. Extensive experimental results suggest that our
proposed approach significantly improve the state-of-the-art results.
Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
Authors: Youjun Xu, Jianfeng Pei, Luhua Lai
Date: 2017-04-16 00:00:00

For quantitative structure-property relationship (QSPR) studies in
chemoinformatics, it is important to get interpretable relationship between
chemical properties and chemical features. However, the predictive power and
interpretability of QSPR models are usually two different objectives that are
difficult to achieve simultaneously. A deep learning architecture using
molecular graph encoding convolutional neural networks (MGE-CNN) provided a
universal strategy to construct interpretable QSPR models with high predictive
power. Instead of using application-specific preset molecular descriptors or
fingerprints, the models can be resolved using raw and pertinent features
without manual intervention or selection. In this study, we developed acute
oral toxicity (AOT) models of compounds using the MGE-CNN architecture as a
case study. Three types of high-level predictive models: regression model
(deepAOT-R), multi-classification model (deepAOT-C) and multi-task model
(deepAOT-CR) for AOT evaluation were constructed. These models highly
outperformed previously reported models. For the two external datasets
containing 1673 (test set I) and 375 (test set II) compounds, the R2 and mean
absolute error (MAE) of deepAOT-R on the test set I were 0.864 and 0.195, and
the prediction accuracy of deepAOT-C was 95.5% and 96.3% on the test set I and
II, respectively. The two external prediction accuracy of deepAOT-CR is 95.0%
and 94.1%, while the R2 and MAE are 0.861 and 0.204 for test set I,
respectively.
Deep 360 Pilot: Learning a Deep Agent for Piloting through 360° Sports Video
Authors: Hou-Ning Hu, Yen-Chen Lin, Ming-Yu Liu, Hsien-Tzu Cheng, Yung-Ju Chang, Min Sun
Date: 2017-05-04 00:00:00
Proceeding: CVPR 2017

Watching a 360{\deg} sports video requires a viewer to continuously select a
viewing angle, either through a sequence of mouse clicks or head movements. To
relieve the viewer from this "360 piloting" task, we propose "deep 360 pilot"
-- a deep learning-based agent for piloting through 360{\deg} sports videos
automatically. At each frame, the agent observes a panoramic image and has the
knowledge of previously selected viewing angles. The task of the agent is to
shift the current viewing angle (i.e. action) to the next preferred one (i.e.,
goal). We propose to directly learn an online policy of the agent from data. We
use the policy gradient technique to jointly train our pipeline: by minimizing
(1) a regression loss measuring the distance between the selected and ground
truth viewing angles, (2) a smoothness loss encouraging smooth transition in
viewing angle, and (3) maximizing an expected reward of focusing on a
foreground object. To evaluate our method, we build a new 360-Sports video
dataset consisting of five sports domains. We train domain-specific agents and
achieve the best performance on viewing angle selection accuracy and transition
smoothness compared to [51] and other baselines.
DualNet: Domain-Invariant Network for Visual Question Answering
Authors: Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada
Date: 2016-06-20 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) task not only bridges the gap between images
and language, but also requires that specific contents within the image are
understood as indicated by linguistic context of the question, in order to
generate the accurate answers. Thus, it is critical to build an efficient
embedding of images and texts. We implement DualNet, which fully takes
advantage of discriminative power of both image and textual features by
separately performing two operations. Building an ensemble of DualNet further
boosts the performance. Contrary to common belief, our method proved effective
in both real images and abstract scenes, in spite of significantly different
properties of respective domain. Our method was able to outperform previous
state-of-the-art methods in real images category even without explicitly
employing attention mechanism, and also outperformed our own state-of-the-art
method in abstract scenes category, which recently won the first place in VQA
Challenge 2016.
Linear Regression with Shuffled Labels
Authors: Abubakar Abid, Ada Poon, James Zou
Date: 2017-05-03 00:00:00
Tasks: regression

Is it possible to perform linear regression on datasets whose labels are
shuffled with respect to the inputs? We explore this question by proposing
several estimators that recover the weights of a noisy linear model from labels
that are shuffled by an unknown permutation. We show that the analog of the
classical least-squares estimator produces inconsistent estimates in this
setting, and introduce an estimator based on the self-moments of the input
features and labels. We study the regimes in which each estimator excels, and
generalize the estimators to the setting where partial ordering information is
available in the form of experiments replicated independently. The result is a
framework that enables robust inference, as we demonstrate by experiments on
both synthetic and standard datasets, where we are able to recover approximate
weights using only shuffled labels. Our work demonstrates that linear
regression in the absence of complete ordering information is possible and can
be of practical interest, particularly in experiments that characterize
populations of particles, such as flow cytometry.
Evolutionary learning of fire fighting strategies
Authors: Martin Kretschmer, Elmar Langetepe
Date: 2017-05-04 00:00:00
Tasks: Evolutionary Algorithms

The dynamic problem of enclosing an expanding fire can be modelled by a
discrete variant in a grid graph. While the fire expands to all neighbouring
cells in any time step, the fire fighter is allowed to block $c$ cells in the
average outside the fire in the same time interval. It was shown that the
success of the fire fighter is guaranteed for $c>1.5$ but no strategy can
enclose the fire for $c\leq 1.5$. For achieving such a critical threshold the
correctness (sometimes even optimality) of strategies and lower bounds have
been shown by integer programming or by direct but often very sophisticated
arguments. We investigate the problem whether it is possible to find or to
approach such a threshold and/or optimal strategies by means of evolutionary
algorithms, i.e., we just try to learn successful strategies for different
constants $c$ and have a look at the outcome. The main general idea is that
this approach might give some insight in the power of evolutionary strategies
for similar geometrically motivated threshold questions. We investigate the
variant of protecting a highway with still unknown threshold and found
interesting strategic paradigms.
  Keywords: Dynamic environments, fire fighting, evolutionary strategies,
threshold approximation
Near-optimal linear decision trees for k-SUM and related problems
Authors: Daniel M. Kane, Shachar Lovett, Shay Moran
Date: 2017-05-04 00:00:00
Tasks: 2k

We construct near optimal linear decision trees for a variety of decision
problems in combinatorics and discrete geometry. For example, for any constant
$k$, we construct linear decision trees that solve the $k$-SUM problem on $n$
elements using $O(n \log^2 n)$ linear queries. Moreover, the queries we use are
comparison queries, which compare the sums of two $k$-subsets; when viewed as
linear queries, comparison queries are $2k$-sparse and have only $\{-1,0,1\}$
coefficients. We give similar constructions for sorting sumsets $A+B$ and for
solving the SUBSET-SUM problem, both with optimal number of queries, up to
poly-logarithmic terms.
  Our constructions are based on the notion of "inference dimension", recently
introduced by the authors in the context of active classification with
comparison queries. This can be viewed as another contribution to the fruitful
link between machine learning and discrete geometry, which goes back to the
discovery of the VC dimension.
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
Authors: Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl
Date: 2017-01-05 00:00:00
Tasks: BIG-bench Machine Learning

OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them
online to work and collaborate more efficiently. In this paper, we present an R
package to interface with the OpenML platform and illustrate its usage in
combination with the machine learning R package mlr. We show how the OpenML
package allows R users to easily search, download and upload data sets and
machine learning tasks. Furthermore, we also show how to upload results of
experiments, share them with others and download results from other users.
Beyond ensuring reproducibility of results, the OpenML platform automates much
of the drudge work, speeds up research, facilitates collaboration and increases
the users' visibility online.
Image-based immersed boundary model of the aortic root
Authors: Ali Hasan, Ebrahim M. Kolahdouz, Andinet Enquobahrie, Thomas G. Caranasos, John P. Vavalle, Boyce E. Griffith
Date: 2017-05-04 00:00:00

Each year, approximately 300,000 heart valve repair or replacement procedures
are performed worldwide, including approximately 70,000 aortic valve
replacement surgeries in the United States alone. This paper describes progress
in constructing anatomically and physiologically realistic immersed boundary
(IB) models of the dynamics of the aortic root and ascending aorta. This work
builds on earlier IB models of fluid-structure interaction (FSI) in the aortic
root, which previously achieved realistic hemodynamics over multiple cardiac
cycles, but which also were limited to simplified aortic geometries and
idealized descriptions of the biomechanics of the aortic valve cusps. By
contrast, the model described herein uses an anatomical geometry reconstructed
from patient-specific computed tomography angiography (CTA) data, and employs a
description of the elasticity of the aortic valve leaflets based on a
fiber-reinforced constitutive model fit to experimental tensile test data.
Numerical tests show that the model is able to resolve the leaflet biomechanics
in diastole and early systole at practical grid spacings. The model is also
used to examine differences in the mechanics and fluid dynamics yielded by
fresh valve leaflets and glutaraldehyde-fixed leaflets similar to those used in
bioprosthetic heart valves. Although there are large differences in the leaflet
deformations during diastole, the differences in the open configurations of the
valve models are relatively small, and nearly identical hemodynamics are
obtained in all cases considered.
Generative Convolutional Networks for Latent Fingerprint Reconstruction
Authors: Jan Svoboda, Federico Monti, Michael M. Bronstein
Date: 2017-05-04 00:00:00

Performance of fingerprint recognition depends heavily on the extraction of
minutiae points. Enhancement of the fingerprint ridge pattern is thus an
essential pre-processing step that noticeably reduces false positive and
negative detection rates. A particularly challenging setting is when the
fingerprint images are corrupted or partially missing. In this work, we apply
generative convolutional networks to denoise visible minutiae and predict the
missing parts of the ridge pattern. The proposed enhancement approach is tested
as a pre-processing step in combination with several standard feature
extraction methods such as MINDTCT, followed by biometric comparison using MCC
and BOZORTH3. We evaluate our method on several publicly available latent
fingerprint datasets captured using different sensors.
Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks
Authors: Joseph P. Robinson, Ming Shao, Yue Wu, Yun Fu
Date: 2016-04-07 00:00:00
Tasks: Kinship Verification, Metric Learning, Triplet

We present the largest kinship recognition dataset to date, Families in the
Wild (FIW). Motivated by the lack of a single, unified dataset for kinship
recognition, we aim to provide a dataset that captivates the interest of the
research community. With only a small team, we were able to collect, organize,
and label over 10,000 family photos of 1,000 families with our annotation tool
designed to mark complex hierarchical relationships and local label information
in a quick and efficient manner. We include several benchmarks for two
image-based tasks, kinship verification and family recognition. For this, we
incorporate several visual features and metric learning methods as baselines.
Also, we demonstrate that a pre-trained Convolutional Neural Network (CNN) as
an off-the-shelf feature extractor outperforms the other feature types. Then,
results were further boosted by fine-tuning two deep CNNs on FIW data: (1) for
kinship verification, a triplet loss function was learned on top of the network
of pre-trained weights; (2) for family recognition, a family-specific softmax
classifier was added to the network.
Probabilistic Typology: Deep Generative Models of Vowel Inventories
Authors: Ryan Cotterell, Jason Eisner
Date: 2017-05-04 00:00:00
Proceeding: ACL 2017 7
Tasks: Point Processes

Linguistic typology studies the range of structures present in human
language. The main goal of the field is to discover which sets of possible
phenomena are universal, and which are merely frequent. For example, all
languages have vowels, while most---but not all---languages have an /u/ sound.
In this paper we present the first probabilistic treatment of a basic question
in phonological typology: What makes a natural vowel inventory? We introduce a
series of deep stochastic point processes, and contrast them with previous
computational, simulation-based approaches. We provide a comprehensive suite of
experiments on over 200 distinct languages.
Tramp Ship Scheduling Problem with Berth Allocation Considerations and Time-dependent Constraints
Authors: Francisco López-Ramos, Armando Guarnaschelli, José-Fernando Camacho-Vallejo, Laura Hervert-Escobar, Rosa G. González-Ramírez
Date: 2017-05-04 00:00:00
Tasks: Scheduling

This work presents a model for the Tramp Ship Scheduling problem including
berth allocation considerations, motivated by a real case of a shipping
company. The aim is to determine the travel schedule for each vessel
considering multiple docking and multiple time windows at the berths. This work
is innovative due to the consideration of both spatial and temporal attributes
during the scheduling process. The resulting model is formulated as a
mixed-integer linear programming problem, and a heuristic method to deal with
multiple vessel schedules is also presented. Numerical experimentation is
performed to highlight the benefits of the proposed approach and the
applicability of the heuristic. Conclusions and recommendations for further
research are provided.
The Ontological Multidimensional Data Model
Authors: Leopoldo Bertossi, Mostafa Milani
Date: 2017-03-10 00:00:00
Tasks: model

In this extended abstract we describe, mainly by examples, the main elements
of the Ontological Multidimensional Data Model, which considerably extends a
relational reconstruction of the multidimensional data model proposed by
Hurtado and Mendelzon by means of tuple-generating dependencies,
equality-generating dependencies, and negative constraints as found in
Datalog+-. We briefly mention some good computational properties of the model.
Satisfying Real-world Goals with Dataset Constraints
Authors: Gabriel Goh, Andrew Cotter, Maya Gupta, Michael Friedlander
Date: 2016-06-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Fairness

The goal of minimizing misclassification error on a training set is often
just one of several real-world goals that might be defined on different
datasets. For example, one may require a classifier to also make positive
predictions at some specified rate for some subpopulation (fairness), or to
achieve a specified empirical recall. Other real-world goals include reducing
churn with respect to a previously deployed model, or stabilizing online
training. In this paper we propose handling multiple goals on multiple datasets
by training with dataset constraints, using the ramp penalty to accurately
quantify costs, and present an efficient algorithm to approximately optimize
the resulting non-convex constrained optimization problem. Experiments on both
benchmark and real-world industry datasets demonstrate the effectiveness of our
approach.
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Authors: Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler
Date: 2017-05-03 00:00:00
Tasks: CPU, GPU

Popular deep learning frameworks require users to fine-tune their memory
usage so that the training data of a deep neural network (DNN) fits within the
GPU physical memory. Prior work tries to address this restriction by
virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be
utilized for memory allocations. Despite its merits, virtualizing memory can
incur significant performance overheads when the time needed to copy data back
and forth from CPU memory is higher than the latency to perform the
computations required for DNN forward and backward propagation. We introduce a
high-performance virtualization strategy based on a "compressing DMA engine"
(cDMA) that drastically reduces the size of the data structures that are
targeted for CPU-side allocations. The cDMA engine offers an average 2.6x
(maximum 13.8x) compression ratio by exploiting the sparsity inherent in
offloaded data, improving the performance of virtualized DNNs by an average 32%
(maximum 61%).
Semi-supervised cross-entropy clustering with information bottleneck constraint
Authors: Marek Śmieja, Bernhard C. Geiger
Date: 2017-05-03 00:00:00
Tasks: Clustering

In this paper, we propose a semi-supervised clustering method, CEC-IB, that
models data with a set of Gaussian distributions and that retrieves clusters
based on a partial labeling provided by the user (partition-level side
information). By combining the ideas from cross-entropy clustering (CEC) with
those from the information bottleneck method (IB), our method trades between
three conflicting goals: the accuracy with which the data set is modeled, the
simplicity of the model, and the consistency of the clustering with side
information. Experiments demonstrate that CEC-IB has a performance comparable
to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but
is faster, more robust to noisy labels, automatically determines the optimal
number of clusters, and performs well when not all classes are present in the
side information. Moreover, in contrast to other semi-supervised models, it can
be successfully applied in discovering natural subgroups if the partition-level
side information is derived from the top levels of a hierarchical clustering.
Compressive Image Recovery Using Recurrent Generative Model
Authors: Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra
Date: 2016-12-13 00:00:00
Tasks: Image Reconstruction, model

Reconstruction of signals from compressively sensed measurements is an
ill-posed problem. In this paper, we leverage the recurrent generative model,
RIDE, as an image prior for compressive image reconstruction. Recurrent
networks can model long-range dependencies in images and hence are suitable to
handle global multiplexing in reconstruction from compressive imaging. We
perform MAP inference with RIDE using back-propagation to the inputs and
projected gradient method. We propose an entropy thresholding based approach
for preserving texture in images well. Our approach shows superior
reconstructions compared to recent global reconstruction approaches like D-AMP
and TVAL3 on both simulated and real data.
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
Authors: Dushyant Mehta, Srinath Sridhar, Oleksandr Sotnychenko, Helge Rhodin, Mohammad Shafiei, Hans-Peter Seidel, Weipeng Xu, Dan Casas, Christian Theobalt
Date: 2017-05-03 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.
Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means
Authors: Frank Nielsen, Richard Nock
Date: 2017-02-16 00:00:00
Tasks: Diversity

Comparative convexity is a generalization of convexity relying on abstract
notions of means. We define the Jensen divergence and the Jensen diversity from
the viewpoint of comparative convexity, and show how to obtain the generalized
Bregman divergences as limit cases of skewed Jensen divergences. In particular,
we report explicit formula of these generalized Bregman divergences when
considering quasi-arithmetic means. Finally, we introduce a generalization of
the Bhattacharyya statistical distances based on comparative means using
relative convexity.