pwc-archive/papers-with-abstracts huggingface dataset:
Compilation as a Typed EDSL-to-EDSL Transformation
Authors: Emil Axelsson
Date: 2016-03-29 00:00:00
Tasks: C++ code, Translation

This article is about an implementation and compilation technique that is
used in RAW-Feldspar which is a complete rewrite of the Feldspar embedded
domain-specific language (EDSL) (Axelsson et al. 2010). Feldspar is high-level
functional language that generates efficient C code to run on embedded targets.
The gist of the technique presented in this post is the following: rather
writing a back end that converts pure Feldspar expressions directly to C, we
translate them to a low-level monadic EDSL. From the low-level EDSL, C code is
then generated. This approach has several advantages:
  1. The translation is simpler to write than a complete C back end.
  2. The translation is between two typed EDSLs, which rules out many potential
errors.
  3. The low-level EDSL is reusable and can be shared between several
high-level EDSLs.
  Although the article contains a lot of code, most of it is in fact reusable.
As mentioned in Discussion, we can write the same implementation in less than
50 lines of code using generic libraries that we have developed to support
Feldspar.
A telescoping Bregmanian proximal gradient method without the global Lipschitz continuity assumption
Authors: Daniel Reem, Simeon Reich, Alvaro De Pierro
Date: 2018-04-19 00:00:00

The problem of minimization of the sum of two convex functions has various
theoretical and real-world applications. One of the popular methods for solving
this problem is the proximal gradient method (proximal forward-backward
algorithm). A very common assumption in the use of this method is that the
gradient of the smooth term is globally Lipschitz continuous. However, this
assumption is not always satisfied in practice, thus casting a limitation on
the method. In this paper, we discuss, in a wide class of finite and
infinite-dimensional spaces, a new variant of the proximal gradient method
which does not impose the above-mentioned global Lipschitz continuity
assumption. A key contribution of the method is the dependence of the iterative
steps on a certain telescopic decomposition of the constraint set into subsets.
Moreover, we use a Bregman divergence in the proximal forward-backward
operation. Under certain practical conditions, a non-asymptotic rate of
convergence (that is, in the function values) is established, as well as the
weak convergence of the whole sequence to a minimizer. We also obtain a few
auxiliary results of independent interest.
Information Directed Sampling and Bandits with Heteroscedastic Noise
Authors: Johannes Kirschner, Andreas Krause
Date: 2018-01-29 00:00:00
Tasks: Bayesian Optimization, Thompson Sampling

In the stochastic bandit problem, the goal is to maximize an unknown function
via a sequence of noisy evaluations. Typically, the observation noise is
assumed to be independent of the evaluation point and to satisfy a tail bound
uniformly on the domain; a restrictive assumption for many applications. In
this work, we consider bandits with heteroscedastic noise, where we explicitly
allow the noise distribution to depend on the evaluation point. We show that
this leads to new trade-offs for information and regret, which are not taken
into account by existing approaches like upper confidence bound algorithms
(UCB) or Thompson Sampling. To address these shortcomings, we introduce a
frequentist regret analysis framework, that is similar to the Bayesian
framework of Russo and Van Roy (2014), and we prove a new high-probability
regret bound for general, possibly randomized policies, which depends on a
quantity we refer to as regret-information ratio. From this bound, we define a
frequentist version of Information Directed Sampling (IDS) to minimize the
regret-information ratio over all possible action sampling distributions. This
further relies on concentration inequalities for online least squares
regression in separable Hilbert spaces, which we generalize to the case of
heteroscedastic noise. We then formulate several variants of IDS for linear and
reproducing kernel Hilbert space response functions, yielding novel algorithms
for Bayesian optimization. We also prove frequentist regret bounds, which in
the homoscedastic case recover known bounds for UCB, but can be much better
when the noise is heteroscedastic. Empirically, we demonstrate in a linear
setting with heteroscedastic noise, that some of our methods can outperform UCB
and Thompson Sampling, while staying competitive when the noise is
homoscedastic.
Deep Triplet Ranking Networks for One-Shot Recognition
Authors: Meng Ye, Yuhong Guo
Date: 2018-04-19 00:00:00
Tasks: One-Shot Learning, Triplet

Despite the breakthroughs achieved by deep learning models in conventional
supervised learning scenarios, their dependence on sufficient labeled training
data in each class prevents effective applications of these deep models in
situations where labeled training instances for a subset of novel classes are
very sparse -- in the extreme case only one instance is available for each
class. To tackle this natural and important challenge, one-shot learning, which
aims to exploit a set of well labeled base classes to build classifiers for the
new target classes that have only one observed instance per class, has recently
received increasing attention from the research community. In this paper we
propose a novel end-to-end deep triplet ranking network to perform one-shot
learning. The proposed approach learns class universal image embeddings on the
well labeled base classes under a triplet ranking loss, such that the instances
from new classes can be categorized based on their similarity with the one-shot
instances in the learned embedding space. Moreover, our approach can naturally
incorporate the available one-shot instances from the new classes into the
embedding learning process to improve the triplet ranking model. We conduct
experiments on two popular datasets for one-shot learning. The results show the
proposed approach achieves better performance than the state-of-the- art
comparison methods.
A Practical Acyclicity Notion for Query Answering over Horn-SRIQ Ontologies
Authors: David Carral, Cristina Feier, Pascal Hitzler
Date: 2018-04-19 00:00:00

Conjunctive query answering over expressive Horn Description Logic ontologies
is a relevant and challenging problem which, in some cases, can be addressed by
application of the chase algorithm. In this paper, we define a novel acyclicity
notion which provides a sufficient condition for termination of the restricted
chase over Horn-SRIQ TBoxes. We show that this notion generalizes most of the
existing acyclicity conditions (both theoretically and empirically).
Furthermore, this new acyclicity notion gives rise to a very efficient
reasoning procedure. We provide evidence for this by providing a
materialization based reasoner for acyclic ontologies which outperforms other
state-of-the-art systems.
Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills
Authors: Sao Mai Nguyen, Pierre-Yves Oudeyer
Date: 2018-04-19 00:00:00
Tasks: Imitation Learning

This paper presents a technical approach to robot learning of motor skills
which combines active intrinsically motivated learning with imitation learning.
Our architecture, called SGIM-D, allows efficient learning of high-dimensional
continuous sensorimotor inverse models in robots, and in particular learns
distributions of parameterised motor policies that solve a corresponding
distribution of parameterised goals/tasks. This is made possible by the
technical integration of imitation learning techniques within an algorithm for
learning inverse models that relies on active goal babbling. After reviewing
social learning and intrinsic motivation approaches to action learning, we
describe the general framework of our algorithm, before detailing its
architecture. In an experiment where a robot arm has to learn to use a flexible
fishing line , we illustrate that SGIM-D efficiently combines the advantages of
social learning and intrinsic motivation and benefits from human demonstration
properties to learn how to produce varied outcomes in the environment, while
developing more precise control policies in large spaces.
Specifying, Monitoring, and Executing Workflows in Linked Data Environments
Authors: Tobias Käfer, Andreas Harth
Date: 2018-04-13 00:00:00
Tasks: Data Integration, Mixed Reality

We present an ontology for representing workflows over components with
Read-Write Linked Data interfaces and give an operational semantics to the
ontology via a rule language. Workflow languages have been successfully applied
for modelling behaviour in enterprise information systems, in which the data is
often managed in a relational database. Linked Data interfaces have been widely
deployed on the web to support data integration in very diverse domains,
increasingly also in scenarios involving the Internet of Things, in which
application behaviour is often specified using imperative programming
languages. With our work we aim to combine workflow languages, which allow for
the high-level specification of application behaviour by non-expert users, with
Linked Data, which allows for decentralised data publication and integrated
data access. We show that our ontology is expressive enough to cover the basic
workflow patterns and demonstrate the applicability of our approach with a
prototype system that observes pilots carrying out tasks in a mixed-reality
aircraft cockpit. On a synthetic benchmark from the building automation domain,
the runtime scales linearly with the size of the number of Internet of Things
devices.
Helping or Hurting? Predicting Changes in Users' Risk of Self-Harm Through Online Community Interactions
Authors: Luca Soldaini, Timothy Walsh, Arman Cohan, Julien Han, Nazli Goharian
Date: 2018-04-19 00:00:00
Proceeding: WS 2018 6

In recent years, online communities have formed around suicide and self-harm
prevention. While these communities offer support in moment of crisis, they can
also normalize harmful behavior, discourage professional treatment, and
instigate suicidal ideation. In this work, we focus on how interaction with
others in such a community affects the mental state of users who are seeking
support. We first build a dataset of conversation threads between users in a
distressed state and community members offering support. We then show how to
construct a classifier to predict whether distressed users are helped or harmed
by the interactions in the thread, and we achieve a macro-F1 score of up to
0.69.
Semantic Text Analysis for Detection of Compromised Accounts on Social Networks
Authors: Dominic Seyler, Lunan Li, ChengXiang Zhai
Date: 2018-04-19 00:00:00
Tasks: Language Modeling, Language Modelling

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for semantic analysis of text messages coming out from an account to detect compromised accounts. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We propose to use the difference of language models of users and adversaries to define novel interpretable semantic features for measuring semantic incoherence in a message stream. We study the effectiveness of the proposed semantic features using a Twitter data set. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.
SuperPoint: Self-Supervised Interest Point Detection and Description
Authors: Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
Date: 2017-12-20 00:00:00
Tasks: Domain Adaptation, Homography Estimation, Interest Point Detection

This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level interest point locations and associated descriptors in one forward
pass. We introduce Homographic Adaptation, a multi-scale, multi-homography
approach for boosting interest point detection repeatability and performing
cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on
the MS-COCO generic image dataset using Homographic Adaptation, is able to
repeatedly detect a much richer set of interest points than the initial
pre-adapted deep model and any other traditional corner detector. The final
system gives rise to state-of-the-art homography estimation results on HPatches
when compared to LIFT, SIFT and ORB.
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems
Authors: Mustafa A. Mohamad, Themistoklis P. Sapsis
Date: 2018-04-19 00:00:00
Tasks: Bayesian Inference

We develop a method for the evaluation of extreme event statistics associated
with nonlinear dynamical systems, using a small number of samples. From an
initial dataset of design points, we formulate a sequential strategy that
provides the 'next-best' data point (set of parameters) that when evaluated
results in improved estimates of the probability density function (pdf) for a
scalar quantity of interest. The approach utilizes Gaussian process regression
to perform Bayesian inference on the parameter-to-observation map describing
the quantity of interest. We then approximate the desired pdf along with
uncertainty bounds utilizing the posterior distribution of the inferred map.
The 'next-best' design point is sequentially determined through an optimization
procedure that selects the point in parameter space that maximally reduces
uncertainty between the estimated bounds of the pdf prediction. Since the
optimization process utilizes only information from the inferred map it has
minimal computational cost. Moreover, the special form of the metric emphasizes
the tails of the pdf. The method is practical for systems where the
dimensionality of the parameter space is of moderate size, i.e. order O(10). We
apply the method to estimate the extreme event statistics for a very
high-dimensional system with millions of degrees of freedom: an offshore
platform subjected to three-dimensional irregular waves. It is demonstrated
that the developed approach can accurately determine the extreme event
statistics using limited number of samples.
Multi-view Hybrid Embedding: A Divide-and-Conquer Approach
Authors: Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen
Date: 2018-04-19 00:00:00
Tasks: Classification, General Classification

We present a novel cross-view classification algorithm where the gallery and
probe data come from different views. A popular approach to tackle this problem
is the multi-view subspace learning (MvSL) that aims to learn a latent subspace
shared by multi-view data. Despite promising results obtained on some
applications, the performance of existing methods deteriorates dramatically
when the multi-view data is sampled from nonlinear manifolds or suffers from
heavy outliers. To circumvent this drawback, motivated by the
Divide-and-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a
unique method of dividing the problem of cross-view classification into three
subproblems and building one model for each subproblem. Specifically, the first
model is designed to remove view discrepancy, whereas the second and third
models attempt to discover the intrinsic nonlinear structure and to increase
discriminability in intra-view and inter-view samples respectively. The kernel
extension is conducted to further boost the representation power of MvHE.
Extensive experiments are conducted on four benchmark datasets. Our methods
demonstrate overwhelming advantages against the state-of-the-art MvSL based
cross-view classification approaches in terms of classification accuracy and
robustness.
A refined convergence analysis of pDCA$_e$ with applications to simultaneous sparse recovery and outlier detection
Authors: Tianxiang Liu, Ting Kei Pong, Akiko Takeda
Date: 2018-04-19 00:00:00
Tasks: compressed sensing, CPU, Outlier Detection

We consider the problem of minimizing a difference-of-convex (DC) function,
which can be written as the sum of a smooth convex function with Lipschitz
gradient, a proper closed convex function and a continuous possibly nonsmooth
concave function. We refine the convergence analysis in [38] for the proximal
DC algorithm with extrapolation (pDCA$_e$) and show that the whole sequence
generated by the algorithm is convergent when the objective is level-bounded,
{\em without} imposing differentiability assumptions in the concave part. Our
analysis is based on a new potential function and we assume such a function is
a Kurdyka-{\L}ojasiewicz (KL) function. We also establish a relationship
between our KL assumption and the one used in [38]. Finally, we demonstrate how
the pDCA$_e$ can be applied to a class of simultaneous sparse recovery and
outlier detection problems arising from robust compressed sensing in signal
processing and least trimmed squares regression in statistics. Specifically, we
show that the objectives of these problems can be written as level-bounded DC
functions whose concave parts are {\em typically nonsmooth}. Moreover, for a
large class of loss functions and regularizers, the KL exponent of the
corresponding potential function are shown to be 1/2, which implies that the
pDCA$_e$ is locally linearly convergent when applied to these problems. Our
numerical experiments show that the pDCA$_e$ usually outperforms the proximal
DC algorithm with nonmonotone linesearch [24, Appendix A] in both CPU time and
solution quality for this particular application.
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
Authors: Sarthak Jain, Edward Banner, Jan-Willem van de Meent, Iain J. Marshall, Byron C. Wallace
Date: 2018-04-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Retrieval

We propose a method for learning disentangled representations of texts that
code for distinct and complementary aspects, with the aim of affording
efficient model transfer and interpretability. To induce disentangled
embeddings, we propose an adversarial objective based on the (dis)similarity
between triplets of documents with respect to specific aspects. Our motivating
application is embedding biomedical abstracts describing clinical trials in a
manner that disentangles the populations, interventions, and outcomes in a
given trial. We show that our method learns representations that encode these
clinically salient aspects, and that these can be effectively used to perform
aspect-specific retrieval. We demonstrate that the approach generalizes beyond
our motivating application in experiments on two multi-aspect review corpora.
Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
Authors: Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, Thomas Vetter
Date: 2017-12-05 00:00:00
Tasks: Face Recognition

It is unknown what kind of biases modern in the wild face datasets have
because of their lack of annotation. A direct consequence of this is that total
recognition rates alone only provide limited insight about the generalization
ability of a Deep Convolutional Neural Networks (DCNNs). We propose to
empirically study the effect of different types of dataset biases on the
generalization ability of DCNNs. Using synthetically generated face images, we
study the face recognition rate as a function of interpretable parameters such
as face pose and light. The proposed method allows valuable details about the
generalization performance of different DCNN architectures to be observed and
compared. In our experiments, we find that: 1) Indeed, dataset bias has a
significant influence on the generalization performance of DCNNs. 2) DCNNs can
generalize surprisingly well to unseen illumination conditions and large
sampling gaps in the pose variation. 3) Using the presented methodology we
reveal that the VGG-16 architecture outperforms the AlexNet architecture at
face recognition tasks because it can much better generalize to unseen face
poses, although it has significantly more parameters. 4) We uncover a main
limitation of current DCNN architectures, which is the difficulty to generalize
when different identities to not share the same pose variation. 5) We
demonstrate that our findings on synthetic data also apply when learning from
real-world data. Our face image generator is publicly available to enable the
community to benchmark other DCNN architectures.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Authors: Shaojie Bai, J. Zico Kolter, Vladlen Koltun
Date: 2018-03-04 00:00:00
Tasks: Audio Synthesis, Language Modelling, Machine Translation, Music Modeling, Sequential Image Classification, Time Series Analysis, Translation

For most deep learning practitioners, sequence modeling is synonymous with
recurrent networks. Yet recent results indicate that convolutional
architectures can outperform recurrent networks on tasks such as audio
synthesis and machine translation. Given a new sequence modeling task or
dataset, which architecture should one use? We conduct a systematic evaluation
of generic convolutional and recurrent architectures for sequence modeling. The
models are evaluated across a broad range of standard tasks that are commonly
used to benchmark recurrent networks. Our results indicate that a simple
convolutional architecture outperforms canonical recurrent networks such as
LSTMs across a diverse range of tasks and datasets, while demonstrating longer
effective memory. We conclude that the common association between sequence
modeling and recurrent networks should be reconsidered, and convolutional
networks should be regarded as a natural starting point for sequence modeling
tasks. To assist related work, we have made code available at
http://github.com/locuslab/TCN .
Data-Driven Analysis of Pareto Set Topology
Authors: Naoki Hamada, Keisuke Goto
Date: 2018-04-19 00:00:00

When and why can evolutionary multi-objective optimization (EMO) algorithms
cover the entire Pareto set? That is a major concern for EMO researchers and
practitioners. A recent theoretical study revealed that (roughly speaking) if
the Pareto set forms a topological simplex (a curved line, a curved triangle, a
curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover
the entire Pareto set. Usually, we cannot know the true Pareto set and have to
estimate its topology by using the population of EMO algorithms during or after
the runtime. This paper presents a data-driven approach to analyze the topology
of the Pareto set. We give a theory of how to recognize the topology of the
Pareto set from data and implement an algorithm to judge whether the true
Pareto set may form a topological simplex or not. Numerical experiments show
that the proposed method correctly recognizes the topology of high-dimensional
Pareto sets within reasonable population size.
Vehicle Communication Strategies for Simulated Highway Driving
Authors: Cinjon Resnick, Ilya Kulikov, Kyunghyun Cho, Jason Weston
Date: 2018-04-19 00:00:00
Tasks: BIG-bench Machine Learning, Self-Driving Cars

Interest in emergent communication has recently surged in Machine Learning.
The focus of this interest has largely been either on investigating the
properties of the learned protocol or on utilizing emergent communication to
better solve problems that already have a viable solution. Here, we consider
self-driving cars coordinating with each other and focus on how communication
influences the agents' collective behavior. Our main result is that
communication helps (most) with adverse conditions.