pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Object Matching for Relational Data
Authors: Tomoharu Iwata, Naonori Ueda
Date: 2018-10-09 00:00:00
Tasks: Density Estimation, Object

We propose an unsupervised object matching method for relational data, which
finds matchings between objects in different relational datasets without
correspondence information. For example, the proposed method matches documents
in different languages in multi-lingual document-word networks without
dictionaries nor alignment information. The proposed method assumes that each
object has latent vectors, and the probability of neighbor objects is modeled
by the inner-product of the latent vectors, where the neighbors are generated
by short random walks over the relations. The latent vectors are estimated by
maximizing the likelihood of the neighbors for each dataset. The estimated
latent vectors contain hidden structural information of each object in the
given relational dataset. Then, the proposed method linearly projects the
latent vectors for all the datasets onto a common latent space shared across
all datasets by matching the distributions while preserving the structural
information. The projection matrix is estimated by minimizing the distance
between the latent vector distributions with an orthogonality regularizer. To
represent the distributions effectively, we use the kernel embedding of
distributions that hold high-order moment information about a distribution as
an element in a reproducing kernel Hilbert space, which enables us to calculate
the distance between the distributions without density estimation. The
structural information encoded in the latent vectors are preserved by using the
orthogonality regularizer. We demonstrate the effectiveness of the proposed
method with experiments using real-world multi-lingual document-word relational
datasets and multiple user-item relational datasets.
Context-Aware Text-Based Binary Image Stylization and Synthesis
Authors: Shuai Yang, Jiaying Liu, Wenhan Yang, Zongming Guo
Date: 2018-10-09 00:00:00
Tasks: Image Inpainting, Image Stylization, Layout Design, Style Transfer, Texture Synthesis

In this work, we present a new framework for the stylization of text-based
binary images. First, our method stylizes the stroke-based geometric shape like
text, symbols and icons in the target binary image based on an input style
image. Second, the composition of the stylized geometric shape and a background
image is explored. To accomplish the task, we propose legibility-preserving
structure and texture transfer algorithms, which progressively narrow the
visual differences between the binary image and the style image. The
stylization is then followed by a context-aware layout design algorithm, where
cues for both seamlessness and aesthetics are employed to determine the optimal
layout of the shape in the background. Given the layout, the binary image is
seamlessly embedded into the background by texture synthesis under a
context-aware boundary constraint. According to the contents of binary images,
our method can be applied to many fields. We show that the proposed method is
capable of addressing the unsupervised text stylization problem and is superior
to state-of-the-art style transfer methods in automatic artistic typography
creation. Besides, extensive experiments on various tasks, such as
visual-textual presentation synthesis, icon/symbol rendering and
structure-guided image inpainting, demonstrate the effectiveness of the
proposed method.
Generalized Latent Variable Recovery for Generative Adversarial Networks
Authors: Nicholas Egan, Jeffrey Zhang, Kevin Shen
Date: 2018-10-09 00:00:00
Tasks: Generative Adversarial Network

The Generator of a Generative Adversarial Network (GAN) is trained to
transform latent vectors drawn from a prior distribution into realistic looking
photos. These latent vectors have been shown to encode information about the
content of their corresponding images. Projecting input images onto the latent
space of a GAN is non-trivial, but previous work has successfully performed
this task for latent spaces with a uniform prior. We extend these techniques to
latent spaces with a Gaussian prior, and demonstrate our technique's
effectiveness.
Cubic Regularization with Momentum for Nonconvex Optimization
Authors: Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
Date: 2018-10-09 00:00:00

Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. However, such a successful acceleration technique has not yet been proposed for second-order algorithms in nonconvex optimization.In this paper, we apply the momentum scheme to cubic regularized (CR) Newton's method and explore the potential for acceleration. Our numerical experiments on various nonconvex optimization problems demonstrate that the momentum scheme can substantially facilitate the convergence of cubic regularization, and perform even better than the Nesterov's acceleration scheme for CR. Theoretically, we prove that CR under momentum achieves the best possible convergence rate to a second-order stationary point for nonconvex optimization. Moreover, we study the proposed algorithm for solving problems satisfying an error bound condition and establish a local quadratic convergence rate. Then, particularly for finite-sum problems, we show that the proposed algorithm can allow computational inexactness that reduces the overall sample complexity without degrading the convergence rate.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
Authors: Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim, Federico Tombari, Kostas Bekris, Bertram Drost, Thibault Groueix, Krzysztof Walas, Vincent Lepetit, Ales Leonardis, Carsten Steger, Frank Michel, Caner Sahin, Carsten Rother, Jiri Matas
Date: 2018-10-09 00:00:00
Tasks: 6D Pose Estimation using RGB, Object, Pose Estimation

This document summarizes the 4th International Workshop on Recovering 6D
Object Pose which was organized in conjunction with ECCV 2018 in Munich. The
workshop featured four invited talks, oral and poster presentations of accepted
workshop papers, and an introduction of the BOP benchmark for 6D object pose
estimation. The workshop was attended by 100+ people working on relevant topics
in both academia and industry who shared up-to-date advances and discussed open
problems.
SPIGAN: Privileged Adversarial Learning from Simulation
Authors: Kuan-Hui Lee, German Ros, Jie Li, Adrien Gaidon
Date: 2018-10-09 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation, Image-to-Image Translation, Semantic Segmentation, Unsupervised Domain Adaptation

Deep Learning for Computer Vision depends mainly on the source of
supervision.Photo-realistic simulators can generate large-scale automatically
labeled syntheticdata, but introduce a domain gap negatively impacting
performance. We propose anew unsupervised domain adaptation algorithm, called
SPIGAN, relying on Sim-ulator Privileged Information (PI) and Generative
Adversarial Networks (GAN).We use internal data from the simulator as PI during
the training of a target tasknetwork. We experimentally evaluate our approach
on semantic segmentation. Wetrain the networks on real-world Cityscapes and
Vistas datasets, using only unla-beled real-world images and synthetic labeled
data with z-buffer (depth) PI fromthe SYNTHIA dataset. Our method improves over
no adaptation and state-of-the-art unsupervised domain adaptation techniques.
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
Authors: Alexander Neergaard Olesen, Poul Jennum, Paul Peppard, Emmanuel Mignot, Helge Bjarup Dissing Sorensen
Date: 2018-10-08 00:00:00
Tasks: Automatic Sleep Stage Classification, General Classification

We have developed an automatic sleep stage classification algorithm based on
deep residual neural networks and raw polysomnogram signals. Briefly, the raw
data is passed through 50 convolutional layers before subsequent classification
into one of five sleep stages. Three model configurations were trained on 1850
polysomnogram recordings and subsequently tested on 230 independent recordings.
Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of
0.746, improving on previous reported results by other groups also using only
raw polysomnogram data. Most errors were made on non-REM stage 1 and 3
decisions, errors likely resulting from the definition of these stages. Further
testing on independent cohorts is needed to verify performance for clinical
use.
Neural Networks Models for Analyzing Magic: the Gathering Cards
Authors: Felipe Zilio, Marcelo Prates, Luis Lamb
Date: 2018-10-08 00:00:00
Tasks: BIG-bench Machine Learning, Card Games, Text Matching

Historically, games of all kinds have often been the subject of study in
scientific works of Computer Science, including the field of machine learning.
By using machine learning techniques and applying them to a game with defined
rules or a structured dataset, it's possible to learn and improve on the
already existing techniques and methods to tackle new challenges and solve
problems that are out of the ordinary. The already existing work on card games
tends to focus on gameplay and card mechanics. This work aims to apply neural
networks models, including Convolutional Neural Networks and Recurrent Neural
Networks, in order to analyze Magic: the Gathering cards, both in terms of card
text and illustrations; the card images and texts are used to train the
networks in order to be able to classify them into multiple categories. The
ultimate goal was to develop a methodology that could generate card text
matching it to an input image, which was attained by relating the prediction
values of the images and generated text across the different categories.
JOBS: Joint-Sparse Optimization from Bootstrap Samples
Authors: Luoluo Liu, Sang Peter Chin, Trac. D. Tran
Date: 2018-10-08 00:00:00

Classical signal recovery based on $\ell_1$ minimization solves the least
squares problem with all available measurements via sparsity-promoting
regularization. In practice, it is often the case that not all measurements are
available or required for recovery. Measurements might be corrupted/missing or
they arrive sequentially in streaming fashion. In this paper, we propose a
global sparse recovery strategy based on subsets of measurements, named JOBS,
in which multiple measurements vectors are generated from the original pool of
measurements via bootstrapping, and then a joint-sparse constraint is enforced
to ensure support consistency among multiple predictors. The final estimate is
obtained by averaging over the $K$ predictors. The performance limits
associated with different choices of number of bootstrap samples $L$ and number
of estimates $K$ is analyzed theoretically. Simulation results validate some of
the theoretical analysis, and show that the proposed method yields
state-of-the-art recovery performance, outperforming $\ell_1$ minimization and
a few other existing bootstrap-based techniques in the challenging case of low
levels of measurements and is preferable over other bagging-based methods in
the streaming setting since it performs better with small $K$ and $L$ for
data-sets with large sizes.
Efficient Two-Step Adversarial Defense for Deep Neural Networks
Authors: Ting-Jui Chang, Yukun He, Peng Li
Date: 2018-10-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Defense, Vocal Bursts Valence Prediction

In recent years, deep neural networks have demonstrated outstanding
performance in many machine learning tasks. However, researchers have
discovered that these state-of-the-art models are vulnerable to adversarial
examples: legitimate examples added by small perturbations which are
unnoticeable to human eyes. Adversarial training, which augments the training
data with adversarial examples during the training process, is a well known
defense to improve the robustness of the model against adversarial attacks.
However, this robustness is only effective to the same attack method used for
adversarial training. Madry et al.(2017) suggest that effectiveness of
iterative multi-step adversarial attacks and particularly that projected
gradient descent (PGD) may be considered the universal first order adversary
and applying the adversarial training with PGD implies resistance against many
other first order attacks. However, the computational cost of the adversarial
training with PGD and other multi-step adversarial examples is much higher than
that of the adversarial training with other simpler attack techniques. In this
paper, we show how strong adversarial examples can be generated only at a cost
similar to that of two runs of the fast gradient sign method (FGSM), allowing
defense against adversarial attacks with a robustness level comparable to that
of the adversarial training with multi-step adversarial examples. We
empirically demonstrate the effectiveness of the proposed two-step defense
approach against different attack methods and its improvements over existing
defense strategies.
Novel Single View Constraints for Manhattan 3D Line Reconstruction
Authors: Siddhant Ranade, Srikumar Ramalingam
Date: 2018-10-08 00:00:00

This paper proposes a novel and exact method to reconstruct line-based 3D
structure from a single image using Manhattan world assumption. This problem is
a distinctly unsolved problem because there can be multiple 3D reconstructions
from a single image. Thus, we are often forced to look for priors like
Manhattan world assumption and common scene structures. In addition to the
standard orthogonality, perspective projection, and parallelism constraints, we
investigate a few novel constraints based on the physical realizability of the
3D scene structure. We treat the line segments in the image to be part of a
graph similar to straws and connectors game, where the goal is to back-project
the line segments in 3D space and while ensuring that some of these 3D line
segments connect with each other (i.e., truly intersect in 3D space) to form
the 3D structure. We consider three sets of novel constraints while solving the
reconstruction: (1) constraints on a series of Manhattan line intersections
that form cycles, but are not all physically realizable, (2) constraints on
true and false intersections in the case of nearby lines lying on the same
Manhattan plane, and (3) constraints from the intersections on boundary and
non-boundary line segments. The reconstruction is achieved using mixed integer
linear programming (MILP), and we show compelling results on real images. Along
with this paper, we will release a challenging Single View Line Reconstruction
dataset with ground truth 3D line models for research purposes.
Learning Tractable Probabilistic Models for Moral Responsibility and Blame
Authors: Lewis Hammond, Vaishak Belle
Date: 2018-10-08 00:00:00
Tasks: Decision Making, Management, Moral Scenarios, Self-Driving Cars

Moral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed effectively and efficiently, given the split-second decision points faced by some systems? By building on constrained tractable probabilistic learning, we propose and implement a hybrid (between data-driven and rule-based methods) learning framework for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.
Find the dimension that counts: Fast dimension estimation and Krylov PCA
Authors: Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
Date: 2018-10-08 00:00:00
Tasks: Model Selection

High dimensional data and systems with many degrees of freedom are often
characterized by covariance matrices. In this paper, we consider the problem of
simultaneously estimating the dimension of the principal (dominant) subspace of
these covariance matrices and obtaining an approximation to the subspace. This
problem arises in the popular principal component analysis (PCA), and in many
applications of machine learning, data analysis, signal and image processing,
and others. We first present a novel method for estimating the dimension of the
principal subspace. We then show how this method can be coupled with a Krylov
subspace method to simultaneously estimate the dimension and obtain an
approximation to the subspace. The dimension estimation is achieved at no
additional cost. The proposed method operates on a model selection framework,
where the novel selection criterion is derived based on random matrix
perturbation theory ideas. We present theoretical analyses which (a) show that
the proposed method achieves strong consistency (i.e., yields optimal solution
as the number of data-points $n\rightarrow \infty$), and (b) analyze conditions
for exact dimension estimation in the finite $n$ case. Using recent results, we
show that our algorithm also yields near optimal PCA. The proposed method
avoids forming the sample covariance matrix (associated with the data)
explicitly and computing the complete eigen-decomposition. Therefore, the
method is inexpensive, which is particularly advantageous in modern data
applications where the covariance matrices can be very large. Numerical
experiments illustrate the performance of the proposed method in various
applications.
Efficient Non-parametric Bayesian Hawkes Processes
Authors: Rui Zhang, Christian Walder, Marian-Andrei Rizoiu, Lexing Xie
Date: 2018-10-08 00:00:00

In this paper, we develop an efficient nonparametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it provides flexible Hawkes kernels and quantifies their uncertainty. Our method is based on the cluster representation of Hawkes processes. Utilizing the finite support assumption of the Hawkes process, we efficiently sample random branching structures and thus, we split the Hawkes process into clusters of Poisson processes. We derive two algorithms -- a block Gibbs sampler and a maximum a posteriori estimator based on expectation maximization -- and we show that our methods have a linear time complexity, both theoretically and empirically. On synthetic data, we show our methods to be able to infer flexible Hawkes triggering kernels. On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset. We also observe that on diffusions related to online videos, the learned kernels reflect the perceived longevity for different content types such as music or pets videos.
Probabilistic Semantic Inpainting with Pixel Constrained CNNs
Authors: Emilien Dupont, Suhas Suresha
Date: 2018-10-08 00:00:00

Semantic inpainting is the task of inferring missing pixels in an image given
surrounding pixels and high level image semantics. Most semantic inpainting
algorithms are deterministic: given an image with missing regions, a single
inpainted image is generated. However, there are often several plausible
inpaintings for a given missing region. In this paper, we propose a method to
perform probabilistic semantic inpainting by building a model, based on
PixelCNNs, that learns a distribution of images conditioned on a subset of
visible pixels. Experiments on the MNIST and CelebA datasets show that our
method produces diverse and realistic inpaintings.
Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games
Authors: Judy Hanwen Shen, Matthias Hofer, Bjarke Felbo, Roger Levy
Date: 2018-10-08 00:00:00
Proceeding: CONLL 2018 10

Simple reference games are of central theoretical and empirical importance in
the study of situated language use. Although language provides rich,
compositional truth-conditional semantics to facilitate reference, speakers and
listeners may sometimes lack the overall lexical and cognitive resources to
guarantee successful reference through these means alone. However, language
also has rich associational structures that can serve as a further resource for
achieving successful reference. Here we investigate this use of associational
information in a setting where only associational information is available: a
simplified version of the popular game Codenames. Using optimal experiment
design techniques, we compare a range of models varying in the type of
associative information deployed and in level of pragmatic sophistication
against human behavior. In this setting, we find that listeners' behavior
reflects direct bigram collocational associations more strongly than
word-embedding or semantic knowledge graph-based associations and that there is
little evidence for pragmatically sophisticated behavior by either speakers or
listeners of the type that might be predicted by recursive-reasoning models
such as the Rational Speech Acts theory. These results shed light on the nature
of the lexical resources that speakers and listeners can bring to bear in
achieving reference through associative meaning alone.
Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challenges
Authors: Ali Borji
Date: 2018-10-08 00:00:00
Tasks: Saliency Prediction

Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans and discuss remaining issues that need to be addressed to build the next generation of more powerful saliency models. Some specific questions that are addressed include: in what ways current models fail, how to remedy them, what can be learned from cognitive studies of attention, how explicit saliency judgments relate to fixations, how to conduct fair model comparison, and what are the emerging applications of saliency models.
Design by adaptive sampling
Authors: David H. Brookes, Jennifer Listgarten
Date: 2018-10-08 00:00:00

We present a probabilistic modeling framework and adaptive sampling algorithm wherein unsupervised generative models are combined with black box predictive models to tackle the problem of input design. In input design, one is given one or more stochastic "oracle" predictive functions, each of which maps from the input design space (e.g. DNA sequences or images) to a distribution over a property of interest (e.g. protein fluorescence or image content). Given such stochastic oracles, the problem is to find an input that is expected to maximize one or more properties, or to achieve a specified value of one or more properties, or any combination thereof. We demonstrate experimentally that our approach substantially outperforms other recently presented methods for tackling a specific version of this problem, namely, maximization when the oracle is assumed to be deterministic and unbiased. We also demonstrate that our method can tackle more general versions of the problem.